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Piccini, Jacopo, Elias August, Sami Leon Noel Aziz Hanna, Tiina Siilak, and Erna Sif Arnardóttir. "Automatic Detection of Electrodermal Activity Events during Sleep." Signals 4, no. 4 (December 18, 2023): 877–91. http://dx.doi.org/10.3390/signals4040048.
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Currently, there is significant interest in developing algorithms for processing electrodermal activity (EDA) signals recorded during sleep. The interest is driven by the growing popularity and increased accuracy of wearable devices capable of recording EDA signals. If properly processed and analysed, they can be used for various purposes, such as identifying sleep stages and sleep-disordered breathing, while being minimally intrusive. Due to the tedious nature of manually scoring EDA sleep signals, the development of an algorithm to automate scoring is necessary. In this paper, we present a novel scoring algorithm for the detection of EDA events and EDA storms using signal processing techniques. We apply the algorithm to EDA recordings from two different and unrelated studies that have also been manually scored and evaluate its performances in terms of precision, recall, and F1 score. We obtain F1 scores of about 69% for EDA events and of about 56% for EDA storms. In comparison to the literature values for scoring agreement between experts, we observe a strong agreement between automatic and manual scoring of EDA events and a moderate agreement between automatic and manual scoring of EDA storms. EDA events and EDA storms detected with the algorithm can be further processed and used as training variables in machine learning algorithms to classify sleep health.
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Biegański, Piotr, Anna Stróż, Marian Dovgialo, Anna Duszyk-Bogorodzka, and Piotr Durka. "On the Unification of Common Actigraphic Data Scoring Algorithms." Sensors 21, no. 18 (September 21, 2021): 6313. http://dx.doi.org/10.3390/s21186313.
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Actigraphy is a well-known, inexpensive method to investigate human movement patterns. Sleep and circadian rhythm studies are among the most popular applications of actigraphy. In this study, we investigate seven common sleep-wake scoring algorithms designed for actigraphic data, namely Cole-Kripke algorithm, two versions of Sadeh algorithm, Sazonov algorithm, Webster algorithm, UCSD algorithm and Scripps Clinic algorithm. We propose a unified mathematical framework describing five of them. One of the observed novelties is that five of these algorithms are in fact equivalent to low-pass FIR filters with very similar characteristics. We also provide explanations about the role of some factors defining these algorithms, as none were given by their Authors who followed empirical procedures. Proposed framework provides a robust mathematical description of discussed algorithms, which for the first time allows one to fully understand their operation and basics.
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Kim, Myeong Seok, Tae Kyoung Ha, Ho Dong Lee, and Young Jun Lee. "0945 A Robust Hybrid algorithm for automatic respiratory events scoring in adults." SLEEP 46, Supplement_1 (May 1, 2023): A417. http://dx.doi.org/10.1093/sleep/zsad077.0945.
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Abstract Introduction Even though Polysomnography has many advantages, manual scoring is a labor intensive and time consuming. Therefore, automatic scoring system using a deep learning algorithm has been developed and commercialized for clinical sites. However, it is challenging to explain the results of respiratory events and to measure a precise event’s duration. To overcome these challenges, a new hybrid algorithm was proposed to explain why this event was categorized and provided the accurate duration of respiratory events. Methods In order to classify respiratory events - OSA, MSA, CSA, O-hypopnea, and C-hypopnea, the proposed algorithm consists of two algorithms – deep learning and post processing algorithms. Deep learning algorithm was used to detect candidates of respiratory events and post processing algorithm was used to determine the final classification and to measure duration of events. Deep learning model consisted of five layers: a perceptron layer, three consecutive layers of long short-term memory (LSTM) cells, and two more perceptron layers. A total of 1,000 case PSG data were enrolled to develop this algorithm. To classify respiratory events from the results provided by the deep learning algorithm, auto-correlation, peak detection, de-noise filtering and transition detection were used to obtain respiratory rate and measure signal amplitude. Using these algorithm’s results, amplitude and time of baseline breathing and respiratory events were calculated and classified based on the AASM’s guidelines. Results To evaluate the performance of proposed algorithm, 30 PSG data were selected and compared with the results of deep learning algorithm. The test dataset consisted of thirty subjects with 15 OSA, 5 PLMS, 2 insomnia and 8 healthy. The kappa value of deep learning algorithms showed 0.77 (0.759, 0.78) and that of proposed algorithm was 0.838 (0.832, 0.843). They showed better agreement by 0.068 than that of deep learning algorithm. In addition, the proposed algorithm provided the classification results such as OSA, MSA, CSA, O-hypopnea and C-hypopnea and explanations of the reason of event detection. Conclusion The proposed algorithm showed better agreement and versatile classified results. This algorithm will be adopted for new automatic sleep scoring solution and this solution will be very helpful to use in real clinical sites. Support (if any)
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Chakraborty, Sabyasachi, Satyabrata Aich, and Hee-Cheol Kim. "A Novel Sleep Scoring Algorithm-Based Framework and Sleep Pattern Analysis Using Machine Learning Techniques." International Journal of System Dynamics Applications 10, no. 3 (July 2021): 1–20. http://dx.doi.org/10.4018/ijsda.2021070101.
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Maintaining the suited amount of sleep is considered the prime component for maintaining a proper and adequate health condition. Often it has been observed that people having sleep inconsistency tend to jeopardize the health and appeal to many physiological and psychological disorders. To overcome such difficulties, it is often required to keep a requisite note of the duration and quality of sleep that one is having. This work defines an algorithm that can be utilized in smart wearables or mobile phones to perceive the duration of sleep and also to classify a particular instance as slept or awake on the basis of data fetched from the triaxial accelerometer. A comparative analysis was performed based on the results obtained from some previously developed algorithms, rule-based models, and machine learning models, and it was observed that the algorithm developed in the work outperformed the previously developed algorithms. Moreover, the algorithm developed in the work will very much define the scoring of sleep of an individual for maintaining a proper health balance.
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Magalang, Ulysses, Brendan Keenan, Bethany Staley, Peter Anderer, Marco Ross, Andreas Cerny, Raymond Vasko, Samuel Kuna, and Jessie Bakker. "251 Agreement and reliability of a new polysomnography sleep staging algorithm against multiple human scorers." Sleep 44, Supplement_2 (May 1, 2021): A101. http://dx.doi.org/10.1093/sleep/zsab072.250.
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Abstract Introduction Scoring algorithms have the potential to increase polysomnography (PSG) scoring efficiency while also ensuring consistency and reproducibility. We sought to validate an updated sleep staging algorithm (Somnolyzer; Philips, Monroeville PA USA) against manual sleep staging, by analyzing a dataset we have previously used to report sleep staging variability across nine center-members of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Methods Fifteen PSGs collected at a single sleep clinic were scored independently by technologists at nine SAGIC centers located in six countries, and auto-scored with the algorithm. Each 30-second epoch was staged manually according to American Academy of Sleep Medicine criteria. We calculated the intraclass correlation coefficient (ICC) and performed a Bland-Altman analysis comparing the average manual- and auto-scored total sleep time (TST) and time in each sleep stage (N1, N2, N3, rapid eye movement [REM]). We hypothesized that the values from auto-scoring would show good agreement and reliability when compared to the average across manual scorers. Results The participants contributing to the original dataset had a mean (SD) age of 47 (12) years and 80% were male. Auto-scoring showed substantial (ICC=0.60-0.80) or almost perfect (ICC=0.80-1.00) reliability compared to manual-scoring average, with ICCs (95% confidence interval) of 0.976 (0.931, 0.992) for TST, 0.681 (0.291, 0.879) for time in N1, 0.685 (0.299, 0.881) for time in N2, 0.922 (0.791, 0.973) for time in N3, and 0.930 (0.811, 0.976) for time in REM. Similarly, Bland-Altman analyses showed good agreement between methods, with a mean difference (limits of agreement) of only 1.2 (-19.7, 22.0) minutes for TST, 13.0 (-18.2, 44.1) minutes for N1, -13.8 (-65.7, 38.1) minutes for N2, -0.33 (-26.1, 25.5) minutes for N3, and -1.2 (-25.9, 23.5) minutes for REM. Conclusion Results support high reliability and good agreement between the auto-scoring algorithm and average human scoring for measurements of sleep durations. Auto-scoring slightly overestimated N1 and underestimated N2, but results for TST, N3 and REM were nearly identical on average. Thus, the auto-scoring algorithm is acceptable for sleep staging when compared against human scorers. Support (if any) Philips.
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Magalang, Ulysses, Brendan Keenan, Bethany Staley, Marco Ross, Peter Anderer, Andreas Cerny, Raymond Vasko, Samuel Kuna, and Jessie Bakker. "398 Agreement and reliability of a new respiratory event and arousal detection algorithm against multiple human scorers." Sleep 44, Supplement_2 (May 1, 2021): A158. http://dx.doi.org/10.1093/sleep/zsab072.397.
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Abstract Introduction Scoring algorithms have the potential to increase polysomnography (PSG) scoring efficiency while also ensuring consistency and reproducibility. We sought to validate an updated event detection algorithm (Somnolyzer; Philips, Monroeville PA USA) against manual scoring, by analyzing a dataset we have previously used to report scoring variability across nine center-members of the Sleep Apnea Global Interdisciplinary Consortium (SAGIC). Methods Fifteen PSGs collected at a single sleep clinic were scored independently by technologists at nine SAGIC centers located in six countries, and auto-scored with the algorithm. Arousals, apneas, and hypopneas were identified according to the American Academy of Sleep Medicine recommended criteria. We calculated the intraclass correlation coefficient (ICC) and performed a Bland-Altman analysis comparing the average manual- and auto-scored apnea-hypopnea index (AHI), arousal index (ArI), apneas, obstructive apneas, central apneas, mixed apneas, and hypopneas. We hypothesized that the values from auto-scoring would show good agreement and reliability when compared to the average across manual scorers. Results Participants contributing to the original dataset had a mean (SD) age of 47 (12) years, AHI of 24.7 (18.2) events/hour, and 80% were male. The ICCs (95% confidence interval) between average manual- and auto-scoring were almost perfect (ICC=0.80–1.00) for AHI [0.989 (0.968, 0.996)], ArI [0.897 (0.729, 0.964)], hypopneas [0.992 (0.978, 0.997)], total apneas [0.973 (0.924, 0.991)], and obstructive apneas [0.919 (0.781, 0.972)], and moderately reliable (ICC=0.40–0.60] for central [0.537 (0.069, 0.815)] and mixed [0.502 (0.021, 0.798)] apneas. Similarly, Bland-Altman analyses supported good agreement for event detection between techniques, with a mean difference (limits of agreement) of only 1.45 (-3.22, 6.12) events/hour for AHI, total apneas 5.2 (-23.9, 34.3), obstructive apneas 1.8 (-45.9, 49.5), central apneas 1.8 (-9.7, 13.4), mixed apneas 1.6 (-14.8, 17.9), and hypopneas 4.3 (-12.4, 20.9). Conclusion Results support almost perfect reliability between auto-scoring and manual scoring of AHI, ArI, hypopneas, total apneas, and obstructive apneas, as well as moderate reliability for central and mixed apneas. There was good agreement between methods, with small mean differences; wider limits of agreement for specific type of apneas did not affect accuracy of the overall AHI. Thus, the auto-scoring algorithm appears reliable for event detection. Support (if any) Philips
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Hanif, Umaer, Guillaume Jubien, Alyssa Cairns, Tammie Radke, and Vincent Mysliwiec. "1078 Performance of USleep Algorithm to a Better Than “Gold-Standard” Polysomnogram Validation Data Set." SLEEP 47, Supplement_1 (April 20, 2024): A463. http://dx.doi.org/10.1093/sleep/zsae067.01078.
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Abstract Introduction Algorithm based PSG scoring is increasingly used in clinical practice and research. Currently, there is no accepted PSG validation data set to ensure algorithms are developed from an accepted standard; thus algorithm-based scoring has inherent inaccuracies. The purpose of this study was to evaluate the inter-rater agreement between three experienced sleep technicians to develop a PSG validation data set with a high interscorer reliability and compare performance of the USleep algorithm to the PSG validation data set. Methods One-hundred de-identified PSGs from a clinical database with 55 males and 45 females were independently scored by three different technicians and each record was quality controlled by a lead technician. The data set encompassed all sleep-related events: sleep stages, apneas, hypopneas, desaturations, arousals, and PLMs. Consensus annotations were computed when at least two technicians agreed upon a given event. The performance of each technician was compared against the consensus annotations. USleep was evaluated on the validation data set and the performance with respect to sleep stages was compared against the consensus annotations. Results The inter-rater agreement was 96.0% for sleep stages across all epochs, which did not greatly differ between N1 (88.3%), N2 (97.3%), N3 (94.2%) and REM sleep (98.1%). The inter-rater agreement across all records was 88.9% for arousals, 84.0% for obstructive apneas, 80.2% for central apneas, 76.4% for mixed apneas, 89.4% for hypopneas, 94.9% for desaturations, and 81.3% for PLMs. Comparing USleep to the consensus yielded an accuracy of 78.3%, with differences between N1 (46.2%) and N2 (76.8%), but not N3 (99.3%) and REM sleep (92.1%). Conclusion This data set has high accuracy for sleep stages. The USleep algorithm showed poor performance for N1 and moderate for N2, but high for N3 and REM. Frequently scored PSG measures to include arousals, hypopneas and obstructive apneas had high degrees of accuracy; however, this decreased for central and mixed apneas. This dataset can serve as a benchmark for developing and validating algorithms in PSG scoring for sleep stages and certain PSG measures. Further development is required to provide basis for a comprehensive PSG validation data set. Support (if any)
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Cho, Taeheum, Unang Sunarya, Minsoo Yeo, Bosun Hwang, Yong Seo Koo, and Cheolsoo Park. "Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy." Electronics 8, no. 12 (December 2, 2019): 1461. http://dx.doi.org/10.3390/electronics8121461.
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Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases related to sleep disorders could be identified using sleep-state estimation. This paper presents an end-to-end deep learning architecture using wrist actigraphy, called Deep-ACTINet, for automatic sleep-wake detection using only noise canceled raw activity signals recorded during sleep and without a feature engineering method. As a benchmark test, the proposed Deep-ACTINet is compared with two conventional fixed model based sleep-wake scoring algorithms and four feature engineering based machine learning algorithms. The datasets were recorded from 10 subjects using three-axis accelerometer wristband sensors for eight hours in bed. The sleep recordings were analyzed using Deep-ACTINet and conventional approaches, and the suggested end-to-end deep learning model gained the highest accuracy of 89.65%, recall of 92.99%, and precision of 92.09% on average. These values were approximately 4.74% and 4.05% higher than those for the traditional model based and feature based machine learning algorithms, respectively. In addition, the neuron outputs of Deep-ACTINet contained the most significant information for separating the asleep and awake states, which was demonstrated by their high correlations with conventional significant features. Deep-ACTINet was designed to be a general model and thus has the potential to replace current actigraphy algorithms equipped in wristband wearable devices.
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Hutchison, Stephen, Michael Grandner, Zohar Bromberg, Zoe Morrell, Arnulf Graf, and Dustin Freckleton. "0101 Performance of a Multisensor Ring to Evaluate Sleep At-Home Relative to PSG and Actigraphy: Importance of Generalized Versus Personalized Scoring." Sleep 45, Supplement_1 (May 25, 2022): A45—A46. http://dx.doi.org/10.1093/sleep/zsac079.099.
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Abstract Introduction Multisensor sleep wearable devices have demonstrated utility for research and relative accuracy for discerning sleep-wake patterns at home and in the laboratory. Additional sensors and more complex scoring algorithms may improve the ability of wearables to assess sleep health. Methods Thirty-six healthy adults completed assessment while wearing the experimental device (Happy Ring), as well as Philips Actiwatch, Fitbit, Oura, and Whoop devices. Evaluations at home were conducted using the Dreem headband as an at-home polysomnography reference. The experimental Happy Ring device includes accelerometry, photoplethysmography, electrodermal activity, and skin temperature. Epoch-by-epoch analyses compared the Happy Ring to home polysomnography, as well as other sleep-tracking wearable devices. Scoring was accomplished using two machine-learning-derived algorithms: a “generalized” algorithm, similar to that used in other devices, which was static and applied to all users, and a “personalized” algorithm where parameters are personalized, dynamic, and change based on data collected across different parts of the night of sleep. Results Compared to home polysomnography, the Happy generalized algorithm demonstrated good sensitivity (94%) and specificity (67%), and the Happy personalized algorithm also performed well (93% and 75%, respectively). Other devices demonstrated good sensitivity, ranging from 91% (Whoop) to 96% (Oura). However, specificity was more variable, ranging from 41% (Actiwatch) to 60% (Fitbit). Overall accuracy using the Happy Ring was 91% for generalized and 92% for personalized algorithms, compared to 92% for Oura, 89% for Whoop, 89% for Fitbit, and 89% for Actiwatch. Regarding sleep stages, accuracy for the Happy Ring was 66%, 83%, and 78% for light, deep, and REM sleep, respectively, for the generalized algorithm. For the personalized algorithm accuracy was 78%, 92%, and 95%, for light, deep and REM sleep, respectively. Post-hoc analyses showed that the Happy personalized algorithm demonstrated better specificity than all other modalities (p<0.001). Kappa scores were 0.42 for generalized and 0.60 for personalized, compared to 0.45 for Oura, 0.47 for Whoop, and 0.48 for Fitbit. Conclusion The multisensory Happy ring demonstrated good sensitivity and specificity for the detection of sleep at home. The personalized approach outperformed all others, representing a potential innovation for improving detection accuracy. Support (If Any) Dr. Grandner is supported by R01DA051321 and R01MD011600. This work was supported by Happy Health, Inc.
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Stanus, E., B. Lacroix, M. Kerkhofs, and J. Mendlewicz. "Automated sleep scoring: a comparative reliability study of two algorithms." Electroencephalography and Clinical Neurophysiology 66, no. 4 (April 1987): 448–56. http://dx.doi.org/10.1016/0013-4694(87)90214-8.
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Wang, Jiayi, Jacob Sindorf, Pin-Wei Chen, Jessica Wu, Adrian Gonzales, McKenzie Mattison, Amy Nguyen, et al. "1015 Evaluation of Actigraphy Sensors: Detecting Daytime Sleep After Stroke in an Inpatient Rehabilitation Hospital." SLEEP 47, Supplement_1 (April 20, 2024): A436—A437. http://dx.doi.org/10.1093/sleep/zsae067.01015.
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Abstract Introduction Excessive Daytime Sleepiness (EDS) is common poststroke, significantly impacting rehabilitation participation and quality of life. Early detection of EDS through prolonged daily sleep and wake monitoring is crucial for prompt intervention and improved care. Actigraphy is a promising sleep monitoring technique that circumvents the high cost and low portability of extended polysomnography and the limited reliability of self-reporting. While actigraphy is often used in healthy adults, its application to daytime sleep measurement in the poststroke population remains unexplored. This study assesses the efficacy of actigraphy and its commonly used scoring algorithms to detect daytime sleep in the poststroke population. Methods ActiGraph wGT3X-BT and ActiWatch Spectrum were placed on the less affected wrist of participants. Trained observers monitored daytime sleep occurrences by checking on participants every 10 minutes during non-therapy periods, recording behaviors as active, sedentary, or asleep. The average observation period for each participant was approximately eight hours. Actigraphy data were cross-referenced with on-site, time-specific observations. Both the ActiWatch (Autoscore AMRI) and ActiGraph (Cole Kripke, Sadeh) use non-data-driven algorithms to estimate wake (0) or sleep (1) given a user-determined parameter. We computed an F2 score to summarize the algorithm’s performance at differentiating wake and sleep, placing more weight on the algorithm’s sensitivity to capture daytime sleep. Results Twenty-seven individuals (19F/8M; average age 62.33 ± 3.04 years) were recruited from the poststroke inpatient unit of a rehabilitation hospital. The ActiGraph Cole-Kripke algorithm (configured with minimum sleep time=15 mins, bedtime=10 mins, and wake time=10 mins) yielded the highest F2 score (F2=0.59), outperforming Sadeh (F2=0.57) and ActiWatch ARMI (F2=0.52) algorithms under their respective optimized parameters. When exclusively considering data from participants lying in bed, the ActiGraph device consistently achieved superior performance (F2=0.69) with the same optimized Cole-Kripke settings. Conclusion In poststroke patients in an inpatient rehabilitation unit, ActiGraph (Cole-Kripke) was better than ActiWatch in detecting daytime sleep. The results could help inform specific algorithms and parameters that can be readily implemented for daytime sleep monitoring and EDS detection in poststroke patients. Future considerations for enhanced algorithms should include posture detection to maximize efficacy. Support (if any) NIH R01HD097786-01A1.
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Toban, Gabriel, Khem Poudel, and Don Hong. "REM Sleep Stage Identification with Raw Single-Channel EEG." Bioengineering 10, no. 9 (September 11, 2023): 1074. http://dx.doi.org/10.3390/bioengineering10091074.
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This paper focused on creating an interpretable model for automatic rapid eye movement (REM) and non-REM sleep stage scoring for a single-channel electroencephalogram (EEG). Many methods attempt to extract meaningful information to provide to a learning algorithm. This method attempts to let the model extract the meaningful interpretable information by providing a smaller number of time-invariant signal filters for five frequency ranges using five CNN algorithms. A bi-directional GRU algorithm was applied to the output to incorporate time transition information. Training and tests were run on the well-known sleep-EDF-expanded database. The best results produced 97% accuracy, 93% precision, and 89% recall.
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Bechny, M., L. Fiorillo, G. Monachino, J. van der Meer, M. H. Schmidt, C. L. A. Bassetti, A. Tzovara, and F. D. Faraci. "Do state-of-the-art sleep-scoring algorithms preserve clinical information?" Sleep Medicine 115 (February 2024): 406. http://dx.doi.org/10.1016/j.sleep.2023.11.1091.
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Kuo, C., and G. Chen. "0441 An Automatic Sleep Scoring System Based on Ensemble Convolutional Neural Network and Spectrogram of Sleep Physiological Signal." Sleep 43, Supplement_1 (April 2020): A169. http://dx.doi.org/10.1093/sleep/zsaa056.438.
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Abstract Introduction Manual sleep stage scoring is time consuming and subjective. Therefore, several studies focused on developing automated sleep scoring algorithms. The previously reported the automatic sleep scoring have been develop usually using small dataset, which less than 100 subjects. In this study, an automatic sleep scoring system based on ensemble convolutional neural network (ensemble-CNN) and spectrogram of sleep physiological signal was proposed and evaluated using a large dataset with sleep disorder. Methods The spectrograms were computed from each 30-s EEG and EOG of 994 subjects from PhysioNet 2018 challenge dataset, using the continuous wavelet transform, which were fed into an ensemble-CNN classification for training. The ensemble-CNN contained five pretrained models, ResNet-101, Inception-v4, DenseNet-201, Xception, and NASNet models, because these models’ architectures are different which can learn different features from the spectrograms to obtain high accuracy. The probabilities of five models were averaged to decide the sleep stage for each spectrogram. After classifying sleep stage, a smoothing process was used for sleep continuity. Moreover, the total 80% data from PhysioNet dataset were randomly assigned to the training set, and the remaining data were assigned to the testing set. Results To validate the robustness of the proposed system, the validation procedure was repeated five times. The performance measures were averaged over the five runs. The overall agreement and kappa coefficient of the proposed method are 82% and 0.73, respectively. The sensitivity of the sleep stages of Wake, N1, N2, N3, and REM are 90.0%, 48.6%, 84.9%, 84.2%, and 81.9%, respectively. Conclusion The performance of the proposed method was achieved expert level, and it was noted that the ensemble-CNN is a promising solution for automatic sleep stage scoring. This method can assist clinical staff in reducing the time required for sleep stage scoring in the future. Support This work was supported by the Ministry of Science and Technology, Taiwan. (MOST 106-2218-E-035-013-MY2, 108-2221-E-035-064, and 108-2634-F-006-012).
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Alvarez-Estevez, Diego, and Roselyne M. Rijsman. "Inter-database validation of a deep learning approach for automatic sleep scoring." PLOS ONE 16, no. 8 (August 16, 2021): e0256111. http://dx.doi.org/10.1371/journal.pone.0256111.
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Study objectives Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across different datasets. Sharing data between different centers is also a problem due to potential restrictions due to patient privacy protection. In this work, we describe a new deep learning approach for automatic sleep staging, and address its generalization capabilities on a wide range of public sleep staging databases. We also examine the suitability of a novel approach that uses an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. Methods A general deep learning network architecture for automatic sleep staging is presented. Different preprocessing and architectural variant options are tested. The resulting prediction capabilities are evaluated and compared on a heterogeneous collection of six public sleep staging datasets. Validation is carried out in the context of independent local and external dataset generalization scenarios. Results Best results were achieved using the CNN_LSTM_5 neural network variant. Average prediction capabilities on independent local testing sets achieved 0.80 kappa score. When individual local models predict data from external datasets, average kappa score decreases to 0.54. Using the proposed ensemble-based approach, average kappa performance on the external dataset prediction scenario increases to 0.62. To our knowledge this is the largest study by the number of datasets so far on validating the generalization capabilities of an automatic sleep staging algorithm using external databases. Conclusions Validation results show good general performance of our method, as compared with the expected levels of human agreement, as well as to state-of-the-art automatic sleep staging methods. The proposed ensemble-based approach enables flexible and scalable design, allowing dynamic integration of local models into the final ensemble, preserving data locality, and increasing generalization capabilities of the resulting system at the same time.
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Dimitrov, T. S., M. He, and M. J. Prerau. "0449 Characterizing Spindle Activity as a Time-Frequency Phenomenon." Sleep 43, Supplement_1 (April 2020): A172. http://dx.doi.org/10.1093/sleep/zsaa056.446.
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Abstract Introduction Spindles are currently defined clinically based on observed patterns in the EEG waveform trace, with automated methods seeking to replicate visual scoring by experts. Recent work suggests that sleep spindles may be more readily observed as time-frequency peaks in the EEG spectrogram. This study compares spectral peaks in the multitaper spectrogram to expert and automatic detection scoring, characterizes the variability of spindles across a night, and investigates topographical and temporal clustering of spindles within individual EEG records. Methods We compared spectral peaks, expert scoring, and automatic detection in two datasets (DREAMS, and a high-density control study). Peaks were identified using multitaper spectral estimation and the peak prominence of the normalized power spectrum for each channel. Spatiotemporal variability analysis was performed using cluster and pattern recognition algorithms including penalized sorting of channel activation order, 2D-cross correlation, PCA and UMAP cluster analysis, and the seqNMF method. Results Spectral peaks were shown to be highly robust to and easily differentiated from broadband noise, occuring at rates (10-16 per min) far exceeding spindle rates reported in literature (~2.5 per min). Expert scoring and automated scoring failed to capture clear spectral peaks in the time-frequency domain, indicating an underreporting of the phenomenology. No apparent clustering or patterns of sleep spindle-like activity was observed using the proposed methods, suggesting high variability of spatiotemporal evolution of spindles. Conclusion These results suggest that the difficulty of time-domain visual scoring of spindles causes an artificially low estimate of the underlying phenomenology, which is mirrored in the assumptions implicit in the thresholds of automated scorers. This work shows that spindles are highly variable in their spatiotemporal evolution, suggesting that there is no optimal single electrode for analysis and casting doubt on the presence of a single cortical generation mechanism. We must therefore revisit the concept of the spindle using the time-frequency domain to more robustly characterize underlying phenomenology. Support National Institute Of Neurological Disorders And Stroke Grant R01 NS-096177
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Bouchequet, P., D. Leger, M. Lebrun, and M. Elbaz. "0442 Experimenting Automatic Sleep Analysis Application in a Clinical Context." Sleep 43, Supplement_1 (April 2020): A169—A170. http://dx.doi.org/10.1093/sleep/zsaa056.439.
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Abstract Introduction Multiplication of publications describing groundbreaking automatic sleep analysis processes and algorithms push for real-life experimentation in clinical context, outside of controlled research environments. Methods Various automatic sleep analysis processes from the literature were implemented and orchestrated in a streamlined workflow. Artificial Intelligence algorithms using regular statistical learning or deep learning were re-trained on our own data after repeating the ad-hoc pre-processing steps described in the corresponding articles. For this, we used polysomnographic records previously taped in our clinic, subject to adequate legal authorizations and agreements: 500 nights from single patients with various pathologies. Those trained models were then applied to newly recorded polysomnographies through a platform developed and hosted on premise. For each polysomnography, a standardized and automatized report were generated and transmitted to the clinician in charge of the analysis. This report contains algorithms outputs, including automatic staging and related statistics such as hypnodensity, quantitative electroencephalography (EEG) analysis, spindles detection and automatic diagnosis. Aggregated record statistics are displayed next to our database statistics for benchmarking purposes. Results For sleep staging, we not only reproduced the results of the selected literature but obtained better metrics: a 0.76 Kappa agreement vs 0.69 in the literature. This may be due to our larger training database or the quality of physiologic signals in our data. Clinicians showed interest in the automatic staging part of the analysis. They noticed algorithm errors are mostly focused on ambiguous epochs, just like visual scoring. However, they found help into automated output and explanatory variables (hypnodensity) to score those ambiguous epochs. Conclusion Automatic sleep analysis algorithms used as decision helping tools shows real potential and should be generalized, as long as underlying processes are published and understood by users and clinicians. Support Banque Publique d’Investissement.
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Labrada, Alexei, Elsa Santos Febles, and José Manuel Antelo. "Comparison of Automatic Sleep Stage Classification Methods for Clinical Use." Global Clinical Engineering Journal 5, no. 1 (June 1, 2022): 8–17. http://dx.doi.org/10.31354/globalce.v5i1.125.
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Sleep stage scoring is necessary for diagnosing several sleep disorders. However, it is an intensive and repetitive task and a vital automation candidate. This work seeks to evaluate different kinds of Machine Learning based classification algorithms available in the scientific literature to determine which one fits better the clinical practice requirements. The comparison is made with a predefined experimental design, using electroencephalography, electrooculography, and electromyography signals from the polysomnographic records of the Sleep-EDFx dataset. The comparison considers the accuracy and speed of algorithms based on Linear Discriminate Analysis, Support Vector Machines, Random Forests, and Artificial Neural Networks. The latter group includes the Deep Neural Networks DeapFeatureNet, based on Convolutional Neural Networks, and DeepSleepNet, additionally based on Recurrent Neural Networks. It is determined that several of the tested algorithms boast high accuracy levels (85%). From them, DeepSleepNet is chosen as the fittest due to its considerable advantage in execution time. Nevertheless, the final result should always be reviewed by the experts.
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Chan, Alexander, Ahmet Cakir, David Josephs, Dave Kleinschmidt, Jay Pathmanathan, and Jacob Donoghue. "1079 Robust Automated Sleep Staging Using Only EEG Signals." SLEEP 47, Supplement_1 (April 20, 2024): A463—A464. http://dx.doi.org/10.1093/sleep/zsae067.01079.
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Abstract Introduction Automated algorithms for assisting sleep technologists and clinicians in the staging of sleep have the potential to significantly speed the scoring of PSGs, reduce inter-rater variability, and improve the reliability of diagnosis of sleep disorders. Here, we describe an automated sleep staging machine learning algorithm that scores PSG using only EEG signals. Methods We describe a convolutional neural network-based sleep staging algorithm, SleepStageML™, that was trained on a database of >19,000 polysomnography recordings from a heterogeneous patient population within the Beacon Clinico-PSG Database. The algorithm was evaluated on 100 held-out recordings from 5 clinical institutions across 11 sites, comprising a highly diverse population representative of patients evaluated in sleep clinics. Each recording was independently staged by 3 registered polysomnographic technologists to generate consensus ground-truth sleep staging for each recording. Results The automated algorithm achieved human-level sleep staging performance with per-stage positive-percent-agreements (PPA) of 89% for W, 65% for N1, 81% for N2, 90% for N3, and 92% for R. Negative-percent-agreements (NPA) were 98% for W, 95% for N1, 95% for N2, 94% for N3, and 98% for R. The algorithm achieved a macro-average F1-score of 0.77. In addition, the algorithm’s median absolute error in estimating total sleep time (TST) was 12 minutes, wake after sleep onset (WASO) was 7 minutes, latency to persistent sleep (LPS) was 2 minutes, and REM latency was 1 minute. The algorithm was also able to stage recordings with the minimum number of AASM recommended EEG electrodes without a statistically significant reduction in performance. The sleep staging model was able to generate sleep stages for all 100 recordings within 26 minutes on a computer with an available GPU. Conclusion We demonstrate a model trained on large amounts of highly diverse PSG data that is capable of automatically staging EEG channel data from the PSG to achieve performance matching human experts. An automated staging algorithm that operates on EEG alone has the potential to rapidly provide accurate diagnostic information in a variety of neuropsychiatric conditions with a reduced testing burden, and greatly accelerate the development of novel therapies. Support (if any)
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Grassi, Massimiliano, Daniela Caldirola, Silvia Daccò, Giampaolo Perna, and Archie Defillo. "253 Better and faster automatic sleep staging with artificial intelligence." Sleep 44, Supplement_2 (May 1, 2021): A102. http://dx.doi.org/10.1093/sleep/zsab072.252.
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Abstract Introduction Sleep staging of polysomnography (PSG) is a time-consuming task, it requires significant training, and significant variability among scorers is expected. A new software (MEBsleep by Medibio Limited) was developed to automatically perform sleep scoring based on machine learning algorithms. This study aimed to perform an extensive investigation of its agreement with expert sleep technicians. Methods Forty polysomnography recordings of patients that were referred for sleep evaluation to three sleep clinics were retrospectively collected. Three experienced technicians independently staged the recording complying with the scoring rules of the American Academy of Sleep Medicine guidelines. Positive Percent Agreement (PPA), Positive Predictive Value (PPV), and other agreement statistics between the automatic and manual staging, among the staging performed by the three technicians, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and statistical significance of the differences. Results Automatic staging took less than two minutes per PSG on a consumer laptop. The automatic staging resulted for the most comparable (PPA difference of N1, N3, and REM; PPV difference of N1, N2, N3, and REM) or statistically significantly more in agreement with the technicians’ staging than the between-technician agreement (PPA difference of N2: 3.90%, 95% bootstrap CI 1.79%-6.01%; PPV difference of Wake: 1.16%, 95% bootstrap CI 0.64%/1.67%), with the sole exception of a partial reduction in the positive percent agreement of the Wake stage (PPA difference of Wake -7.04%, 95% bootstrap CI -10.40%/-3.85%). The automatic staging also demonstrated very high accuracy in an indirect comparison with other similar software. Conclusion Given these promising results, the use of this software may support sleep clinicians by improving efficiency in sleep scoring. Support (if any):
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S G, Shashank. "Sleep Disorder Detection Using EEG Signals." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 22, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34611.
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Sleep disorders are prevalent health concerns affecting millions of individuals worldwide, with adverse impacts on overall well-being and cognitive function. Detecting and diagnosing these disorders accurately is crucial for effective treatment planning and management. This project focuses on utilizing Electroencephalogram (EEG) signals, a non-invasive method for monitoring brain activity, to detect sleeping disorders. By leveraging advanced signal processing techniques and machine learning algorithms, this research aims to develop a robust and accurate system capable of identifying various types of sleep disorders, such as insomnia, sleep apnea, and narcolepsy, based on EEG data. The proposed approach holds the potential to enhance early detection, personalized treatment strategies, and ultimately improve the quality of life for individuals affected by sleep disorders. Keywords-Ambulatory EEG, automatic scoring, deep learning, electroencephalography, sleep staging.
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S, Lydia Pradeepa. "Sleeping Stage Classification Using CNN." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 1070–77. http://dx.doi.org/10.22214/ijraset.2024.61348.
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Abstract: Maintaining proper health and mental stability is critical for overall health and well-being. Despite a good deal of research investment, sleep quality continues to be a crucial public challenge. Nowadays, people of all age groups are affected by improper sleep quality. Poor sleep can lead to a variety of neurological disorders. Sleep disorders are common in all subsets of the population, independently of gender. This public health challenge greatly affects quality of life in terms of both physical and mental health. Insomnia, parasomnias, sleep- related breathing difficulties, hypersomnia, bruxism, narcolepsy, and circadian rhythm disorders are some common examples of sleep-related disorders. Some of these disorders can be treated with proper analysis of early symptoms; in such cases, adequate sleep quality is essential for the patient’s recovery. Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleepdisorders, and population health. While still in its nascent stage, there are several challenges which preclude AI’s generalizability and wide-reaching clinical applications. Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms and deep learning algorithm prior to its inclusion in the sleep clinic. In this project, we can develop the framework for sleeping stage classification for both subjective data and ECG data and classify the data using Convolutional neural network algorithm to analyse the multiple sleeping stages.
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Sayeed, Fahad, Justin Brooks, and Nilanjan Banerjee. "0270 Obstructive Sleep Apnea detection using ECG morphology and Machine Learning." SLEEP 46, Supplement_1 (May 1, 2023): A120. http://dx.doi.org/10.1093/sleep/zsad077.0270.
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Abstract Introduction Obstructive Sleep Apnea (OSA) is characterized by reduction in airflow. Hypopnea is a smaller reduction in airflow compared to apnea but also accompanied by drop in oxygen saturation leading to sympathetic activation. Frequent OSA events are correlated with incidence of cardiovascular diseases. Measuring changes in airflow requires overnight polysomnography that is expensive. Electrocardiogram (ECG) is available through wearable devices at home and therefore, detecting OSA events using ECG can make OSA diagnostics more accessible. This paper studies the use of ECG morphology and an ensemble machine learning algorithm to detect OSA events. The algorithm designed is light weight to ensure that it can be implemented on an embedded processor. Methods The data from the Apnea, Bariatric surgery, and CPAP (ABC) study provided by the National Sleep Research Resource is used for the analysis. Twenty-six subjects diagnosed with severe OSA are considered with single night polysomnography before treatment. Since OSA events have a minimum 10s duration, 10s non-overlapping windows with OSA labels from a technician scoring is used. Features are extracted from the PQRST complex and majority voting across multiple algorithms is used to select the top 7 features (SDNN, RMSSD, P-P interval, P-duration, P-R interval, T-duration and T-P interval) with highest explanatory power. We train a random forest classifier using leave-one-out methodology where 25 subjects are used for training and the 26th subject is used for testing (all permutations are used). We use sensitivity, precision and F1 score for evaluation. Results The algorithm detects the precise occurrence (onset and offset) as well as the total number of events during the night. The precision, sensitivity and F1 scores are 70%, 96% and 80% respectively. The prediction leans towards higher occurrence of OSA events as the subjects suffer from severe OSA. We uncover 32% of false positives are events that are accompanied by significant SPO2 desaturation pointing to the inaccuracy of manual scoring. Assuming these false positives are correct predictions the precision increases to 79%. Conclusion The classification of OSA events using ECG features and machine learning demonstrates the feasibility of using wearables to measure OSA in a home setting. Support (if any)
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Haghayegh, S., S. Khoshnevis, M. H. Smolensky, K. R. Diller, and R. J. Castriotta. "1196 Machine Learning Derived-Interpretative Algorithm Better Differentiates Sleep and Wake Epochs and Estimates Sleep Parameters from Wrist Actigraphy Data." Sleep 43, Supplement_1 (April 2020): A457—A458. http://dx.doi.org/10.1093/sleep/zsaa056.1190.
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Abstract Introduction Several different interpretive algorithms (IAs) are available for scoring actigraphy-obtained body movement data for sleep and wake epochs. Although most have high sensitivity in detecting sleep epochs, they identify wake epochs poorly. We derived a machine learning (ML) based IA that improves differentiation of sleep and wake epoch to better estimate sleep parameters. Methods Forty-one adults (18 females) 26.6±12.0 years old underwent at-home single-night sleep assessment. Motionlogger® Micro Watch Actigraph recorded in zero crossing mode body movement per 30s epoch, with automated sleep scoring by single-channel electroencephalography (EEG) device (Zmachine® Insight+) as reference. The popular Cole-Kripke IA was applied to score body movement time series data of the following combination of current 1, preceding 4, and following 2 minute long epochs. Data of 21 subjects were utilized to train/derive the ML IA (logistic regression), and data of the other 20 subjects were used to test performance of it and the Cole-Kripke IA. Results In reference to the EEG, the Cole-Kripke actigraphy IA showed sensitivity of 0.98±0.02, specificity of 0.48±0.19, and kappa agreement of 0.53±0.16 in detecting sleep epochs, while the ML-derived IA showed corresponding values of 0.90±0.06, 0.71±0.14, and 0.57±0.11. The Cole-Kripke IA, relative to EEG, method significantly (P<0.05) underestimated sleep onset latency (SOL) by 18.0 min and wake after sleep onset (WASO) by 35.1 min, and overestimated total sleep time (TST) by 53.1 min and sleep efficiency (SE) by 9.6%. The ML-derived IA, relative to EEG significantly underestimated SOL by 15.1 min, but comparably (P>0.05) estimated WASO, TST, and SE. Conclusion The ML-derived IA, in comparison to Cole-Kripke IA, when applied to sleep-time wrist actigraphy data significantly better differentiates wake from sleep epochs and better estimates sleep parameters. Support This work was supported by the Robert and Prudie Leibrock Professorship in Engineering at the University of Texas at Austin.
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Thorey, V., A. Guillot, K. El Kanbi, M. Harris, and P. J. Arnal. "1211 Assessing the Accuracy of a Dry-EEG Headband for Measuring Brain Activity, Heart Rate, Breathing and Automatic Sleep Staging." Sleep 43, Supplement_1 (April 2020): A463. http://dx.doi.org/10.1093/sleep/zsaa056.1205.
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Abstract Introduction The development of new sleep study devices, adapted for daily use, is necessary for diagnosis of sleep disorders. However, this requires to be both suitable for daily use and capable of recording accurate electrophysiological data. This study assesses the signal acquisition of a comfortable sleep headband, using dry electrodes, and the performance of its automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 4 sleep experts. Methods 42 participants slept at a sleep center wearing both the Dreem headband (DH) and a PSG simultaneously. We measured 1) the EEG signal similarity between both devices, 2) heart rate, breathing frequency and respiration rate variability (RRV) agreement, and 3) the performance of the headband automatic sleep scoring compared to PSG sleep experts manual scoring. Results Results demonstrate a strong correlation between the EEG signals acquired by the headband and those from the PSG, and the signals acquired by the headband enable monitoring of alpha (r= 0.75 ± 0.11), beta (r= 0.74 ± 0.14), delta (r = 0.78 ± 0.16), and theta (r = 0.63 ± 0.15) frequencies during sleep. The mean absolute error for heart rate, breathing frequency, and RRV was 2.2 ± 0.8 bpm, 0.3 ± 0.2 cpm and 3.1 ± 0.4 %, respectively. Automatic Sleep Staging reached an overall accuracy of 84.1 ± 7.5% (F1 score: 83.0 ± 8.4) for the headband to be compared with an average of 86.4 ± 5.5% (F1 score: 86.5 ± 5.5) for the 4 sleep experts. Conclusion These results demonstrate the capacity of the headband to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies. Support This Study has been supported by Dreem sas.
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ElMoaqet, Hisham, Jungyoon Kim, Dawn Tilbury, Satya Krishna Ramachandran, Mutaz Ryalat, and Chao-Hsien Chu. "Gaussian Mixture Models for Detecting Sleep Apnea Events Using Single Oronasal Airflow Record." Applied Sciences 10, no. 21 (November 6, 2020): 7889. http://dx.doi.org/10.3390/app10217889.
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Sleep apnea is a common sleep-related disorder that significantly affects the population. It is characterized by repeated breathing interruption during sleep. Such events can induce hypoxia, which is a risk factor for multiple cardiovascular and cerebrovascular diseases. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score sleep-related events. To address these limitations, many previous studies have proposed and implemented automatic scoring processes based on fewer sensors and machine learning classification algorithms. However, alternative device technologies developed for both home and hospital still have limited diagnostic accuracy for detecting apnea events even though many of the previous investigational algorithms are based on multiple physiological channel inputs. In this paper, we propose a new probabilistic algorithm based on (only) oronasal respiration signal for automated detection of apnea events during sleep. The proposed model leverages AASM recommendations for characterizing apnea events with respect to dynamic changes in the local respiratory airflow baseline. Unlike classical threshold-based classification models, we use a Gaussian mixture probability model for detecting sleep apnea based on the posterior probabilities of the respective events. Our results show significant improvement in the ability to detect sleep apnea events compared to a rule-based classifier that uses the same classification features and also compared to two previously published studies for automated apnea detection using the same respiratory flow signal. We use 96 sleep patients with different apnea severity levels as reflected by their Apnea-Hypopnea Index (AHI) levels. The performance was not only analyzed over obstructive sleep apnea (OSA) but also over other types of sleep apnea events including central and mixed sleep apnea (CSA, MSA). Also the performance was comprehensively analyzed and evaluated over patients with varying disease severity conditions, where it achieved an overall performance of TPR=88.5%, TNR=82.5%, and AUC=86.7%. The proposed approach contributes a new probabilistic framework for detecting sleep apnea events using a single airflow record with an improved capability to generalize over different apnea severity conditions
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Peker, Musa. "An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms." Neurocomputing 207 (September 2016): 165–77. http://dx.doi.org/10.1016/j.neucom.2016.04.049.
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Coppieters ’t Wallant, Dorothée, Pierre Maquet, and Christophe Phillips. "Sleep Spindles as an Electrographic Element: Description and Automatic Detection Methods." Neural Plasticity 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/6783812.
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Sleep spindle is a peculiar oscillatory brain pattern which has been associated with a number of sleep (isolation from exteroceptive stimuli, memory consolidation) and individual characteristics (intellectual quotient). Oddly enough, the definition of a spindle is both incomplete and restrictive. In consequence, there is no consensus about how to detect spindles. Visual scoring is cumbersome and user dependent. To analyze spindle activity in a more robust way, automatic sleep spindle detection methods are essential. Various algorithms were developed, depending on individual research interest, which hampers direct comparisons and meta-analyses. In this review, sleep spindle is first defined physically and topographically. From this general description, we tentatively extract the main characteristics to be detected and analyzed. A nonexhaustive list of automatic spindle detection methods is provided along with a description of their main processing principles. Finally, we propose a technique to assess the detection methods in a robust and comparable way.
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Yaghouby, Farid, Bruce F. O’Hara, and Sridhar Sunderam. "Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model of Electrophysiological Measurements." International Journal of Neural Systems 26, no. 04 (April 27, 2016): 1650017. http://dx.doi.org/10.1142/s0129065716500179.
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The proportion, number of bouts, and mean bout duration of different vigilance states (Wake, NREM, REM) are useful indices of dynamics in experimental sleep research. These metrics are estimated by first scoring state, sometimes using an algorithm, based on electrophysiological measurements such as the electroencephalogram (EEG) and electromyogram (EMG), and computing their values from the score sequence. Isolated errors in the scores can lead to large discrepancies in the estimated sleep metrics. But most algorithms score sleep by classifying the state from EEG/EMG features independently in each time epoch without considering the dynamics across epochs, which could provide contextual information. The objective here is to improve estimation of sleep metrics by fitting a probabilistic dynamical model to mouse EEG/EMG data and then predicting the metrics from the model parameters. Hidden Markov models (HMMs) with multivariate Gaussian observations and Markov state transitions were fitted to unlabeled 24-h EEG/EMG feature time series from 20 mice to model transitions between the latent vigilance states; a similar model with unbiased transition probabilities served as a reference. Sleep metrics predicted from the HMM parameters did not deviate significantly from manual estimates except for rapid eye movement sleep (REM) ([Formula: see text]; Wilcoxon signed-rank test). Changes in value from Light to Dark conditions correlated well with manually estimated differences (Spearman’s rho 0.43–0.84) except for REM. HMMs also scored vigilance state with over 90% accuracy. HMMs of EEG/EMG features can therefore characterize sleep dynamics from EEG/EMG measurements, a prerequisite for characterizing the effects of perturbation in sleep monitoring and control applications.
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Quante, Mirja, Emily R. Kaplan, Michael Cailler, Michael Rueschman, Rui Wang, Jia Weng, Elsie M. Taveras, and Susan Redline. "Actigraphy-based sleep estimation in adolescents and adults: a comparison with polysomnography using two scoring algorithms." Nature and Science of Sleep Volume 10 (January 2018): 13–20. http://dx.doi.org/10.2147/nss.s151085.
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Gu, Wenbo, Peter Wu, Arthur Liu, Wen-Te Liu, Yi-Chun Kuan, Hsin-Chien Lee, Lydia Leung, I.-Chen Wu, and Ambrose Chiang. "0517 Integrating Body Sensor into a Wearable Platform to Enhance the Identification of Central and Mixed Apneas." SLEEP 47, Supplement_1 (April 20, 2024): A222. http://dx.doi.org/10.1093/sleep/zsae067.0517.
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Abstract Introduction Accurate identification of apnea types is crucial for effective diagnosis and management of sleep-disordered breathing. The Belun Sleep System (BLS-100, a.k.a., Belun Ring) is an FDA-cleared home sleep apnea testing system (K222579) comprising an adjustable ring-shaped wearable, a cradle, and two deep learning-powered algorithms. The Belun Cor, a novel subxiphoid sensor equipped with accelerometry, can detect respiratory effort, respiratory rate, body position, and facilitates the detection of central events. This preliminary analysis aims to assess the performance of the integrated BLS-100 in detecting apnea events containing central components. Methods This interim analysis evaluated the performance of BLS-100 in a clinical cohort of hospitalized patients admitted for acute ischemic stroke. Eligible patients underwent in-lab polysomnography (PSG) alongside concurrent BLS-100 testing. PSG scoring adhered to the latest AASM scoring manual, with scoring technicians blinded to the BLS-100 results. The BLS-100 derived total sleep time (bTST), sleep stages (bSTAGE), apnea-hypopnea index (bAHI), and combined central and mixed apnea index (bCMAI). Results As of 12/17/2023, 25 consecutive Taiwanese patients were enrolled. Four patients were excluded due to short bTST< 120 mins. The analysis was conducted on 21 patients. M:F 19:2; age 59.7; PSG TST 270 ± 61.9 mins; PSG AHI 27.0 (1.4-81.9) with 3 normal, 3 mild, 7 moderate, and 8 severe OSA cases. The mean PSG central apnea index (PSG-CAI) was 4.8 (0.0-34.0), with 5 patients having PSG-CAI≥5. The mean PSG central and mixed apnea index (PSG-CMAI) was 8.4 (0.0-47.3). Pearson correlation coefficients between PSG-CAI and bCMAI, as well as PSG-CMAI and bCMAI, were 0.939 (P< 0.001) and 0.982 (P< 0.001), respectively. Using bCMAI≥5 to predict PSG-CAI≥5, the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Cohen’s Kappa were 0.81, 1.00, 0.75, 0.56, 1.00, and 0.59, respectively. Similarly, using bCMAI≥5 to predict PSG-CMAI≥5, the corresponding values were 0.86, 0.88, 0.85, 0.78, 0.92, and 0.70, respectively. Conclusion Early findings indicate that the BLS-100 with Belun Cor shows promising performance in identifying apnea events that include central components. An elevated bCMAI serves as a valuable indicator for clinicians, signaling the presence of central or mixed apneas. Support (if any) Case Western Reserve University-Taipei Medical University Pilot Award
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Mostafa, Mendonça, Ravelo-García, and Morgado-Dias. "A Systematic Review of Detecting Sleep Apnea Using Deep Learning." Sensors 19, no. 22 (November 12, 2019): 4934. http://dx.doi.org/10.3390/s19224934.
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Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.
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Ricketti, P., D. Schwartz, K. Calero, W. Anderson, C. Diaz-Sein, M. Rechkemmer, K. Bell, M. Dahdad, and R. Nakase-Richardson. "1031 A Multicenter Study Examining Two Scoring Algorithms for Diagnosis of Obstructive Sleep Apnea (OSA) in an Acute Neurorehabilitation Population with Traumatic Brain Injury (TBI)." Sleep 41, suppl_1 (April 2018): A383. http://dx.doi.org/10.1093/sleep/zsy061.1030.
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Kim, Dongyoung, Jeonggun Lee, Yunhee Woo, Jaemin Jeong, Chulho Kim, and Dong-Kyu Kim. "Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification." Journal of Personalized Medicine 12, no. 2 (January 20, 2022): 136. http://dx.doi.org/10.3390/jpm12020136.
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Recently, deep learning for automated sleep stage classification has been introduced with promising results. However, as many challenges impede their routine application, automatic sleep scoring algorithms are not widely used. Typically, polysomnography (PSG) uses multiple channels for higher accuracy; however, the disadvantages include a requirement for a patient to stay one or more nights in the lab wearing uncomfortable sensors and wires. To avoid the inconvenience caused by the multiple channels, we aimed to develop a deep learning model for use in clinical decision support systems (CDSSs) and combined convolutional neural networks and a transformer for the supervised learning of three classes of sleep stages only with single-channel EEG data (C4-M1). The data for training, validation, and test were derived from 1590, 341, and 343 polysomnography recordings, respectively. The developed model yielded an overall accuracy of 91.4%, comparable with that of human experts. Based on the severity of obstructive sleep apnea, the model’s accuracy was 94.3%, 91.9%, 91.9%, and 90.6% in normal, mild, moderate, and severe cases, respectively. Our deep learning model enables accurate and rapid delineation of three-class sleep staging and could be useful as a CDSS for application in real-world clinical practice.
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Nollet, Mathieu, William Wisden, and Nicholas P. Franks. "Sleep deprivation and stress: a reciprocal relationship." Interface Focus 10, no. 3 (April 17, 2020): 20190092. http://dx.doi.org/10.1098/rsfs.2019.0092.
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Sleep is highly conserved across evolution, suggesting vital biological functions that are yet to be fully understood. Animals and humans experiencing partial sleep restriction usually exhibit detrimental physiological responses, while total and prolonged sleep loss could lead to death. The perturbation of sleep homeostasis is usually accompanied by an increase in hypothalamic–pituitary–adrenal (HPA) axis activity, leading to a rise in circulating levels of stress hormones (e.g. cortisol in humans, corticosterone in rodents). Such hormones follow a circadian release pattern under undisturbed conditions and participate in the regulation of sleep. The investigation of the consequences of sleep deprivation, from molecular changes to behavioural alterations, has been used to study the fundamental functions of sleep. However, the reciprocal relationship between sleep and the activity of the HPA axis is problematic when investigating sleep using traditional sleep-deprivation protocols that can induce stress per se . This is especially true in studies using rodents in which sleep deprivation is achieved by exogenous, and potentially stressful, sensory–motor stimulations that can undoubtedly confuse their conclusions. While more research is needed to explore the mechanisms underlying sleep loss and health, avoiding stress as a confounding factor in sleep-deprivation studies is therefore crucial. This review examines the evidence of the intricate links between sleep and stress in the context of experimental sleep deprivation, and proposes a more sophisticated research framework for sleep-deprivation procedures that could benefit from recent progress in biotechnological tools for precise neuromodulation, such as chemogenetics and optogenetics, as well as improved automated real-time sleep-scoring algorithms.
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Hannan, Sana, Alyssa Ho, and Birgit Frauscher. "Clinical Utility of Sleep Recordings During Presurgical Epilepsy Evaluation With Stereo-Electroencephalography: A Systematic Review." Journal of Clinical Neurophysiology 41, no. 5 (July 2024): 430–43. http://dx.doi.org/10.1097/wnp.0000000000001057.
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Summary: Although the role of sleep in modulating epileptic activity is well established, many epileptologists overlook the significance of considering sleep during presurgical epilepsy evaluations in cases of drug-resistant epilepsy. Here, we conducted a comprehensive literature review from January 2000 to May 2023 using the PubMed electronic database and compiled evidence to highlight the need to revise the current clinical approach. All articles were assessed for eligibility by two independent reviewers. Our aim was to shed light on the clinical value of incorporating sleep monitoring into presurgical evaluations with stereo-electroencephalography. We present the latest developments on the important bidirectional interactions between sleep and various forms of epileptic activity observed in stereo-electroencephalography recordings. Specifically, epileptic activity is modulated by different sleep stages, peaking in non–rapid eye movement sleep, while being suppressed in rapid eye movement sleep. However, this modulation can vary across different brain regions, underlining the need to account for sleep to accurately pinpoint the epileptogenic zone during presurgical assessments. Finally, we offer practical solutions, such as automated sleep scoring algorithms using stereo-electroencephalography data alone, to seamlessly integrate sleep monitoring into routine clinical practice. It is hoped that this review will provide clinicians with a readily accessible roadmap to the latest evidence concerning the clinical utility of sleep monitoring in the context of stereo-electroencephalography and aid the development of therapeutic and diagnostic strategies to improve patient surgical outcomes.
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Suliman, Ahmad, Md Rakibul Mowla, Alaleh Alivar, Charles Carlson, Punit Prakash, Balasubramaniam Natarajan, Steve Warren, and David E. Thompson. "Effects of Ballistocardiogram Peak Detection Jitters on the Quality of Heart Rate Variability Features: A Simulation-Based Case Study in the Context of Sleep Staging." Sensors 23, no. 5 (March 1, 2023): 2693. http://dx.doi.org/10.3390/s23052693.
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Heart rate variability (HRV) features support several clinical applications, including sleep staging, and ballistocardiograms (BCGs) can be used to unobtrusively estimate these features. Electrocardiography is the traditional clinical standard for HRV estimation, but BCGs and electrocardiograms (ECGs) yield different estimates for heartbeat intervals (HBIs), leading to differences in calculated HRV parameters. This study examines the viability of using BCG-based HRV features for sleep staging by quantifying the impact of these timing differences on the resulting parameters of interest. We introduced a range of synthetic time offsets to simulate the differences between BCG- and ECG-based heartbeat intervals, and the resulting HRV features are used to perform sleep staging. Subsequently, we draw a relationship between the mean absolute error in HBIs and the resulting sleep-staging performances. We also extend our previous work in heartbeat interval identification algorithms to demonstrate that our simulated timing jitters are close representatives of errors between heartbeat interval measurements. This work indicates that BCG-based sleep staging can produce accuracies comparable to ECG-based techniques such that at an HBI error range of up to 60 ms, the sleep-scoring error could increase from 17% to 25% based on one of the scenarios we examined.
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Korotun, M., L. Weizman, A. Drori, J. Zaccaria, T. Goldstein, I. Litman, S. Hahn, and H. Greenberg. "0584 Detecting Sleep Disordered Breathing Using Sub-Terahertz Radio-Frequency Micro-Radar." Sleep 43, Supplement_1 (April 2020): A224. http://dx.doi.org/10.1093/sleep/zsaa056.581.
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Abstract Introduction New sensor technologies are entering sleep testing at a rapid pace; Neteera™ developed a novel sensor and algorithm for sleep apnea detection utilizing a contact-free, radar-based sensor system. The system utilizes a high-frequency, low-power, directional micro-radar which operates at ~120GHz and a sampling rate of 2500Hz as well as algorithms which are able to detect both pulse and respiratory activity of subjects during sleep. Methods Adult subjects undergoing diagnostic PSG for clinical purposes were simultaneously assessed with the novel micro-radar system with sensors under the mattress. Disordered breathing events (DBEs) were scored from the PSG using AASM scoring guidelines and were compared with those detected by the micro-radar sensor. Test data were grouped into three sets: 1. Single under mattress sensor; 2. Two under mattress sensors on each side of the bed (to improve signal capture); 3. After software optimization. The micro-radar sensor detected DBEs but software to describe the type of DBEs (obstructive apnea/central apnea/hypopnea) is still under development. Detection rate of DBEs was compared between the two methodologies and the development sets. Results n=22 (12 F, 10 M), Age=50.8±12.4 years, BMI=35.32±7.37 kg/m2. Diagnostic PSG AHI: 19.7±29.4/hr, T90=15.8±25.7%. Percent DBEs missed by the micro-radar sensor: 1st set=14.6±10.6%; 2nd set=9.4±8.3%; 3rd set=1.2±2.6%. Number of DBEs assessed for each set was 646, 1144, 125 events, respectively. With each successive set, the detection rate improved. Conclusion A novel micro-radar, non-contact sensor technology can be used to detect DBEs during sleep. Detection rate improved with utilization of two sensors per bed and software optimization. Future software development is expected to improve detection rate and facilitate breathing event classification into obstructive apneas/central apneas/hypopneas. Support None.
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Chien, Ying-Ren, Cheng-Hsuan Wu, and Hen-Wai Tsao. "Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning." Sensors 21, no. 18 (September 9, 2021): 6049. http://dx.doi.org/10.3390/s21186049.
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Poor-quality sleep substantially diminishes the overall quality of life. It has been shown that sleep arousal serves as a good indicator for scoring sleep quality. However, patients are conventionally asked to perform overnight polysomnography tests to collect their physiological data, which are used for the manual judging of sleep arousals. Even worse, not only is this process time-consuming and cumbersome, the judgment of sleep-arousal events is subjective and differs widely from expert to expert. Therefore, this work focuses on designing an automatic sleep-arousal detector that necessitates only a single-lead electroencephalogram signal. Based on the stacking ensemble learning framework, the automatic sleep-arousal detector adopts a meta-classifier that stacks four sub-models: one-dimensional convolutional neural networks, recurrent neural networks, merged convolutional and recurrent networks, and random forest classifiers. This meta-classifier exploits both advantages from deep learning networks and conventional machine learning algorithms to enhance its performance. The embedded information for discriminating the sleep-arousals is extracted from waveform sequences, spectrum characteristics, and expert-defined statistics in single-lead EEG signals. Its effectiveness is evaluated using an open-accessed database, which comprises polysomnograms of 994 individuals, provided by PhysioNet. The improvement of the stacking ensemble learning over a single sub-model was up to 9.29%, 7.79%, 11.03%, 8.61% and 9.04%, respectively, in terms of specificity, sensitivity, precision, accuracy, and area under the receiver operating characteristic curve.
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Jilani, Shahla M., Chloe J. Jordan, Lauren M. Jansson, and Jonathan M. Davis. "Definitions of neonatal abstinence syndrome in clinical studies of mothers and infants: an expert literature review." Journal of Perinatology 41, no. 6 (January 29, 2021): 1364–71. http://dx.doi.org/10.1038/s41372-020-00893-8.
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AbstractNeonatal abstinence syndrome (NAS) results from discontinuation of in utero exposures to opioids/substances. The rising incidence of NAS has prompted an increased need for accurate research and public health data. To examine how NAS has been defined in clinical studies of opioid-exposed mothers and infants, a review process was developed based on the RAND/UCLA Appropriateness Method, yielding 888 abstracts. Per inclusion criteria, 57 abstracts underwent full-text review. To define NAS, studies cited using modified versions of the Finnegan NAS scoring tool (n = 21; 37%), ICD-9/10 coding (n = 17; 30%), original Finnegan tool (n = 16; 28%), Eat Sleep Console (n = 3; 5%), and Lipsitz (n = 3; 5%) tools, (3 cited 2+ tools). Most studies utilized subjective NAS scoring/assessment algorithms and neonatal coding as key elements defining NAS. While most cited opioid exposure as integral to their inclusion criteria, 26% did not. These approaches highlight the need for a more refined and standardized definition of NAS.
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Thiesse, Laurie, Luc Staner, Patrice Bourgin, Henri Comtet, Gil Fuchs, Debora Kirscher, Thomas Roth, Jean Yves Schaffhauser, Jay B. Saoud, and Antoine U. Viola. "Somno-Art Software identifies pathology-induced changes in sleep parameters similarly to polysomnography." PLOS ONE 18, no. 10 (October 20, 2023): e0291593. http://dx.doi.org/10.1371/journal.pone.0291593.
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Polysomnographic sleep architecture parameters are commonly used to diagnose or evaluate treatment of sleep disorders. Polysomnography (PSG) having practical constraints, the development of wearable devices and algorithms to monitor and stage sleep is rising. Beside pure validation studies, it is necessary for a clinician to ensure that the conclusions drawn with a new generation wearable sleep scoring device are consistent to the ones of gold standard PSG, leading to similar interpretation and diagnosis. This paper reports on the performance of Somno-Art Software for the detection of differences in sleep parameters between patients suffering from obstructive sleep apnea (OSA), insomniac or major depressive disorder (MDD) compared to healthy subjects. On 244 subjects (n = 26 healthy, n = 28 OSA, n = 66 insomniacs, n = 124 MDD), sleep staging was obtained from PSG and Somno-Art analysis on synchronized electrocardiogram and actimetry signals. Mixed model analysis of variance was performed for each sleep parameter. Possible differences in sleep parameters were further assessed with Mann-Whitney U-test between the healthy subjects and each pathology group. All sleep parameters, except N1+N2, showed significant differences between the healthy and the pathology group. No significant differences were observed between Somno-Art Software and PSG, except a 3.6±2.2 min overestimation of REM sleep. No significant interaction ‘group’*’technology’ was observed, suggesting that the differences in pathologies are independent of the technology used. Overall, comparable differences between healthy subjects and pathology groups were observed when using Somno-Art Software or polysomnography. Somno-Art proposes an interesting valid tool as an aid for diagnosis and treatment follow-up in ambulatory settings.
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Sharma, Manish, Jainendra Tiwari, Virendra Patel, and U. Rajendra Acharya. "Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals." Electronics 10, no. 13 (June 25, 2021): 1531. http://dx.doi.org/10.3390/electronics10131531.
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A sleep disorder is a medical condition that affects an individual’s regular sleeping pattern and routine, hence negatively affecting the individual’s health. The traditional procedures of identifying sleep disorders by clinicians involve questionnaires and polysomnography (PSG), which are subjective, time-consuming, and inconvenient. Hence, an automated sleep disorder identification is required to overcome these limitations. In the proposed study, we have proposed a method using electroencephalogram (EEG) signals for the automated identification of six sleep disorders, namely insomnia, nocturnal frontal lobe epilepsy (NFLE), narcolepsy, rapid eye movement behavior disorder (RBD), periodic leg movement disorder (PLM), and sleep-disordered breathing (SDB). To the best of our belief, this is one of the first studies ever undertaken to identify sleep disorders using EEG signals employing cyclic alternating pattern (CAP) sleep database. After sleep-scoring EEG epochs, we have created eight different data subsets of EEG epochs to develop the proposed model. A novel optimal triplet half-band filter bank (THFB) is used to obtain the subbands of EEG signals. We have extracted Hjorth parameters from subbands of EEG epochs. The selected features are fed to various supervised machine learning algorithms for the automated classification of sleep disorders. Our proposed system has obtained the highest accuracy of 99.2%, 98.2%, 96.2%, 98.3%, 98.8%, and 98.8% for insomnia, narcolepsy, NFLE, PLM, RBD, and SDB classes against normal healthy subjects, respectively, applying ensemble boosted trees classifier. As a result, we have attained the highest accuracy of 91.3% to identify the type of sleep disorder. The proposed method is simple, fast, efficient, and may reduce the challenges faced by medical practitioners during the diagnosis of various sleep disorders accurately in less time at sleep clinics and homes.
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Horger, Melissa, Allison Swift, Kyle Kainec, Jennifer Holmes, and Rebecca Spencer. "0204 Spindle Detection in Polysomnography of Toddlers: Comparing Manual Feature Detection and an Automated Algorithm." SLEEP 47, Supplement_1 (April 20, 2024): A88. http://dx.doi.org/10.1093/sleep/zsae067.0204.
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Abstract Introduction Sleep spindles are a feature of NREM sleep distinctly related to memory (Friedrich et al., 2019; Kurdziel et al., 2013) and later outcomes (Jaramillo et al., 2023). They have a protracted developmental trajectory and, in toddlers, spindles are defined as 10-16 Hz sinusoidal waves with a minimum duration of 500 ms. Manual identification of spindles is time-intensive and subjective. A variety of automated algorithms, utilizing machine learning techniques, have been developed to combat this problem. However, these vary in the extent to which they are trained on robust datasets, accessible to researchers, and customizable. One option, meeting all three criteria, is the Yet Another Spindle Algorithm (YASA). YASA was trained on over 3,000 adult PSG records, is open-access and extensively documented, and is modifiable (Vallat & Walker, 2021). The goal of the current research was to examine the feasibility of YASA to detect spindles in a much younger population- toddlers. This requires direct assessment because spindles are qualitatively different in this age group. Methods Ten toddlers (Range = 21.07-23.5 months; M = 21.84 months) underwent overnight PSG. The first 30 minutes of artifact-free NREM was isolated and scored for sleep spindles. Spindles were identified manually by an expert sleep scorer and automatically via YASA. One adjustment was made to the YASA standard parameters, expanding the range of detectable spindles from 12-15Hz to 10-16Hz. Results Manual scoring served as the ground truth. YASA consistently detected fewer spindles than scorers (C3: Range_YASA = 0-6; M_YASA = 1.6 vs. Range:manual= 0-50; M_manual = 11.2). In one record, zero spindles were identified by both methods. Among the remaining records, the percent agreement, or total spindles identified by both methods out of the total identified by scorers, ranged from 0-44% (M = 8%, SD = 15%). False alarms were present in 3 records (Range = 1-5 spindles). Conclusion Results suggest YASA is not a reliable substitute for manual spindle detection in toddlers. However, additional modifications to the base code may be necessary. We will unpack these modifications and extend this analysis to other auto-detection approaches. Support (if any) This research was funded by NIH R21 HD108913.
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McGovney, K. D., A. F. Curtis, M. Mazurek, W. S. Chan, C. B. Deroche, M. Munoz, M. Davenport, et al. "0922 Nightly Associations Between Pre-Bedtime Activity, Actigraphic Light, and Sleep in Children With ASD and Insomnia." Sleep 43, Supplement_1 (April 2020): A350—A351. http://dx.doi.org/10.1093/sleep/zsaa056.918.
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Abstract Introduction Approximately two thirds of children with Autism Spectrum Disorder (ASD) suffer from chronic insomnia. Current behavioral interventions for insomnia in children with ASD use sleep hygiene guidelines to educate parents and their children regarding sleep promoting habits. However, the relationship between pre-bedtime physical activity/light and sleep is understudied in ASD. The current study examined daily associations between pre-bedtime actigraphically assessed activity/light levels and objective/subjective sleep outcomes in children with ASD and insomnia. Methods Thirty children (Mage=8.5 yrs, SD=1.78 yrs) with comorbid ASD and insomnia completed 14 days of actigraphy measuring ambient white light intensity and activity levels every 30 seconds. Validated sleep scoring algorithms (in Actiware V. 6.0.9) estimated objective sleep onset latency (SOL), total sleep time (TST), wake time after sleep onset (WASO), and average activity/light levels 30, 60, and 120 mins prior to bedtime. Additionally, average activity/light levels 120-240, and 240-360 mins prior to bedtime were computed. Children also completed 14 daily sleep diaries (with parental assistance) measuring subjective reports of the same sleep parameters. Associations between daily estimations of pre-bedtime activity levels, light, and nighttime objective and subjective sleep were examined through multilevel modelling. Bonferroni corrections were performed to account for multiple comparisons. Results After Bonferroni corrections (p<.025 significance level), greater activity within 30 minutes (B=0.0465, p=.0093) and 60 minutes (B=0.0681, p=.0005) of bedtime were associated with longer subjective SOL. Pre-bedtime light exposure was not a significant predictor of sleep outcomes. Conclusion Results suggest that in general, variations in daily pre-bedtime activity, but not light, are associated with worse nightly subjective SOL in children with ASD and insomnia. Findings support that sleep hygiene recommendations in children with ASD include avoidance of higher levels of pre-sleep physical activity. Prospective studies examining temporal causal relationships between pre-bedtime activity and sleep in ASD are warranted. Support Research was supported by a University of Missouri Research Board award (McCrae, PI; Mazurek, Co-PI). Data collected as part of clinical trial NCT02755051 Targeting Sleep in Kids with Autism Spectrum Disorder at the University of Missouri (PI: McCrae).
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Mendonça, Fábio, Sheikh Shanawaz Mostafa, Diogo Freitas, Fernando Morgado-Dias, and Antonio G. Ravelo-García. "Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection." Entropy 24, no. 5 (May 13, 2022): 688. http://dx.doi.org/10.3390/e24050688.
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Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.
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Stepnowsky, Carl, Tania Zamora, Robert Barker, Lin Liu, and Kathleen Sarmiento. "Accuracy of Positive Airway Pressure Device—Measured Apneas and Hypopneas: Role in Treatment Followup." Sleep Disorders 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/314589.
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Improved data transmission technologies have facilitated data collected from positive airway pressure (PAP) devices in the home environment. Although clinicians’ treatment decisions increasingly rely on autoscoring of respiratory events by the PAP device, few studies have specifically examined the accuracy of autoscored respiratory events in the home environment in ongoing PAP use. “PAP efficacy” studies were conducted in which participants wore PAP simultaneously with an Embletta sleep system (Embla, Inc., Broomfield, CO), which was directly connected to the ResMed AutoSet S8 (ResMed, Inc., San Diego, CA) via a specialized cable. Mean PAP-scored Apnea-Hypopnea Index (AHI) was 14.2 ± 11.8 (median: 11.7; range: 3.9–46.3) and mean manual-scored AHI was 9.4 ± 10.2 (median: 7.7; range: 1.2–39.3). Ratios between the mean indices were calculated. PAP-scored HI was 2.0 times higher than the manual-scored HI. PAP-scored AHI was 1.5 times higher than the manual-scored AHI, and PAP-scored AI was 1.04 of manual-scored AI. In this sample, PAP-scored HI was on average double the manual-scored HI. Given the importance of PAP efficacy data in tracking treatment progress, it is important to recognize the possible bias of PAP algorithms in overreporting hypopneas. The most likely cause of this discrepancy is the use of desaturations in manual hypopnea scoring.
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Galaska, B., J. M. Bakker, F. Sert Kuniyoshi, M. Bush, J. Salazar, J. G. Jasko, A. L. Friedman, and D. P. White. "1191 Development of a Clinically-Validated Questionnaire and Scoring Algorithm Designed to Identify Common Sleep Problems Among Adults." Sleep 43, Supplement_1 (April 2020): A456. http://dx.doi.org/10.1093/sleep/zsaa056.1185.
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Abstract Introduction Although sleep is critical to maintaining health and quality of life, inadequate sleep duration and/or quality is common. It can be difficult to distinguish sleep problems that may be addressed through adjustments to lifestyle versus issues that may represent a more serious condition requiring medical intervention. SmartSleep Analyzer is a cloud-based questionnaire and scoring algorithm designed to categorize respondents according to likely sleep problems as follows: obstructive sleep apnea (OSA), snoring, trouble falling asleep or staying asleep, delayed sleep phase disorder (DSPD), shift work disorder (SWD), chronic sleep restriction (CSR), or no sleep problem. Primary, secondary, and tertiary categorizations are provided, where applicable. The objective of this study was to validate the questionnaire scoring algorithm categorization/s against a sleep physician assessment. Methods From 2,316 available records, 90 complete questionnaires were randomly selected for this analysis. The questionnaire scoring algorithm categorization was compared against the consensus assessment of three independent sleep physicians who each reviewed the answers to all questions before arriving at a diagnosis. Results The questionnaire respondents (70% female) were aged 42.2±14.5 years, had a mean BMI of 32.0±7.7 kg/m2, and self-reported sleep duration of 6.5±1.4 hours/night. The primary, secondary, or tertiary categorization of the questionnaire scoring algorithm matched the primary consensus categorization of the physicians 90.6% of the time (95% confidence interval (CI): 82.6 to 95.7). When OSA and snoring were grouped, agreement increased to 98.9% (95% CI: 94.0 to 100). In all analyses undertaken, the accuracy of questionnaire scoring algorithm against the physicians exceeded the accuracy of the physicians when compared to each other. Conclusion These results demonstrate that our questionnaire and scoring algorithm performs well in identifying sleep problems that may impact adult respondents, using physician-review as the comparison standard. Support Philips
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Helland, V. C., A. Gapelyuk, A. Suhrbier, M. Riedl, T. Penzel, J. Kurths, and N. Wessel. "Investigation of an Automatic Sleep Stage Classification by Means of Multiscorer Hypnogram." Methods of Information in Medicine 49, no. 05 (2010): 467–72. http://dx.doi.org/10.3414/me09-02-0052.
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Summary Objectives: Scoring sleep visually based on polysomnography is an important but time-consuming element of sleep medicine. Whereas computer software assists human experts in the assignment of sleep stages to polysomnogram epochs, their performance is usually insufficient. This study evaluates the possibility to fully automatize sleep staging considering the reliability of the sleep stages available from human expert sleep scorers. Methods: We obtain features from EEG, ECG and respiratory signals of polysomnograms from ten healthy subjects. Using the sleep stages provided by three human experts, we evaluate the performance of linear discriminant analysis on the entire polysomnogram and only on epochs where the three experts agree in their sleep stage scoring. Results: We show that in polysomnogram intervals, to which all three scorers assign the same sleep stage, our algorithm achieves 90% accuracy. This high rate of agreement with the human experts is accomplished with only a small set of three frequency features from the EEG. We increase the performance to 93% by including ECG and respiration features. In contrast, on intervals of ambiguous sleep stage, the sleep stage classification obtained from our algorithm, agrees with the human consensus scorer in approximately 61%. Conclusions: These findings suggest that machine classification is highly consistent with human sleep staging and that error in the algorithm’s assignments is rather a problem of lack of well-defined criteria for human experts to judge certain polysomnogram epochs than an insufficiency of computational procedures.
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Svetnik, V., T. Wang, Y. Xu, B. J. Hansen, and S. V. Fox. "0432 A Deep Learning Approach for Automated Sleep-Wake Scoring in Pre-Clinical Animal Models." Sleep 43, Supplement_1 (April 2020): A166. http://dx.doi.org/10.1093/sleep/zsaa056.429.
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Abstract Introduction Experimental investigation of sleep-wake dynamics in animals is an important part of pharmaceutical development. It typically involves recording of electroencephalogram, electromyogram, locomotor activity, and electrooculogram. Visual identification, or scoring, of the sleep-wake states from these recordings is time-consuming. We sought to develop software for automated sleep-wake scoring capable of processing large databases of multi-channel signal recordings in a range of animal species. Methods We used a large historical database of signal recordings and scores in non-human primates, dogs, mice, and rats, to develop a deep Convolutional Neural Network (CNN) classification algorithm for automatically scoring sleep-wake states. We compared the performance of the CNN algorithm with that of a widely used Machine Learning algorithm, Random Forest (RF). Results In non-human primates and dogs, CNN accuracy in sleep-wake scoring of data was significantly higher than RF accuracy: 0.75 versus 0.66 for non-human primates and 0.73 versus 0.64 for dogs. In rodents, the difference between CNN and RF was smaller: 0.83 versus 0.81 for mice and 0.78 versus 0.77 for rats. The variability of CNN accuracy was lower than that of RF for non-human primates, dogs and mice, but similar for rats. Conclusion We recommend use of CNN for sleep-wake scoring in non-human primates and dogs, and RF for sleep-wake scoring in rodents. Support Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA
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Cho, Jae Hoon, Ji Ho Choi, Ji Eun Moon, Young Jun Lee, Ho Dong Lee, and Tae Kyoung Ha. "Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm." Medicina 58, no. 6 (June 9, 2022): 779. http://dx.doi.org/10.3390/medicina58060779.
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Background and Objectives: Polysomnography is manually scored by sleep experts. However, manual scoring is a time-consuming and labor-intensive task. The goal of this study was to verify the accuracy of automated sleep-stage scoring based on a deep learning algorithm compared to manual sleep-stage scoring. Materials and Methods: A total of 602 polysomnography datasets from subjects (Male:Female = 397:205) aged 19 to 65 years (mean age, 43.8, standard deviation = 12.2) were included in the study. The performance of the proposed model was evaluated based on kappa value and bootstrapped point-estimate of median percent agreement with a 95% bootstrap confidence interval and R = 1000. The proposed model was trained using 482 datasets and validated using 48 datasets. For testing, 72 datasets were selected randomly. Results: The proposed model exhibited good concordance rates with manual scoring for stages W (94%), N1 (83.9%), N2 (89%), N3 (92%), and R (93%). The average kappa value was 0.84. For the bootstrap method, high overall agreement between the automated deep learning algorithm and manual scoring was observed in stages W (98%), N1 (94%), N2 (92%), N3 (99%), and R (98%) and total (96%). Conclusions: Automated sleep-stage scoring using the proposed model may be a reliable method for sleep-stage classification.