Academic literature on the topic 'Sleep Scoring Algorithms'
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Journal articles on the topic "Sleep Scoring Algorithms"
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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.
Full textDissertations / Theses on the topic "Sleep Scoring Algorithms"
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Olsson, Sebastian. "Automated sleep scoring using unsupervised learning of meta-features." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-189234.
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Sleep is an important part of life as it affects the performance of one's activities during all awake hours. The study of sleep and wakefulness is therefore of great interest, particularly to the clinical and medical fields where sleep disorders are diagnosed. When studying sleep, it is common to talk about different types, or stages, of sleep. A common task in sleep research is to determine the sleep stage of the sleeping subject as a function of time. This process is known as sleep stage scoring. In this study, I seek to determine whether there is any benefit to using unsupervised feature learning in the context of electroencephalogram-based (EEG) sleep scoring. More specifically, the effect of generating and making use of new feature representations for hand-crafted features of sleep data – meta-features – is studied. For this purpose, two scoring algorithms have been implemented and compared. Both scoring algorithms involve segmentation of the EEG signal, feature extraction, feature selection and classification using a support vector machine (SVM). Unsupervised feature learning was implemented in the form of a dimensionality-reducing deep-belief network (DBN) which the feature space was processed through. Both scorers were shown to have a classification accuracy of about 76 %. The application of unsupervised feature learning did not affect the accuracy significantly. It is speculated that with a better choice of parameters for the DBN in a possible future work, the accuracy may improve significantly.Sömnen är en viktig del av livet eftersom den påverkar ens prestation under alla vakna timmar. Forskning om sömn and vakenhet är därför av stort intresse, i synnerhet för de kliniska och medicinska områdena där sömnbesvär diagnostiseras. I forskning om sömn är det är vanligt att tala om olika typer av sömn, eller sömnstadium. En vanlig uppgift i sömnforskning är att avgöra sömnstadiet av den sovande exemplaret som en funktion av tiden. Den här processen kallas sömnmätning. I den här studien försöker jag avgöra om det finns någon fördel med att använda oövervakad inlärning av särdrag för att utföra elektroencephalogram-baserad (EEG) sömnmätning. Mer specifikt undersöker jag effekten av att generera och använda nya särdragsrepresentationer som härstammar från handgjorda särdrag av sömndata – meta-särdrag. Två sömnmätningsalgoritmer har implementerats och jämförts för det här syftet. Sömnmätningsalgoritmerna involverar segmentering av EEG-signalen, extraktion av särdragen, urval av särdrag och klassificering genom användning av en stödvektormaskin (SVM). Oövervakad inlärning av särdrag implementerades i form av ett dimensionskrympande djuptrosnätverk (DBN) som användes för att bearbetasärdragsrymden. Båda sömnmätarna visades ha en klassificeringsprecision av omkring 76 %. Användningen av oövervakad inlärning av särdrag hade ingen signifikant inverkan på precisionen. Det spekuleras att precisionen skulle kunna höjas med ett mer lämpligt val av parametrar för djuptrosnätverket.
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Melone, Marie-Anne. "Diagnοstic and therapeutic strategies οf circadian and sleep/wake rhythm disοrders in at-risk pοpulatiοns." Electronic Thesis or Diss., Normandie, 2024. http://www.theses.fr/2024NORMR023.
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Les recommandations en matière de santé du sommeil préconisent 7 à 9 heures de sommeil par nuit pour la population générale. Pourtant, la dette chronique de sommeil persiste et présente des risques importants pour la santé, notamment en termes de maladies métaboliques, cardiovasculaires et neurocognitives. Cette dette chronique de sommeil est souvent attribuée au conflit entre le mode de vie moderne - l'éclairage artificiel et les obligations sociales - et nos rythmes circadiens endogènes, ce qui conduit à une perturbation appelée le désalignement circadien. Les rythmes circadiens sont les oscillations naturelles des processus physiologiques. Ces rythmes sont essentiels pour aligner nos processus physiologiques et comportementaux sur le temps solaire afin d'anticiper les changements dans notre environnement. Le désalignement circadien est de plus en plus souvent incriminé dans divers états pathologiques. Le diagnostic des troubles des rythmes circadiens ou rythmes veille/sommeil reste difficile en pratique, car sa définition repose en partie sur des évaluations subjectives de la qualité du sommeil. Les questionnaires et les agendas de sommeil bien qu’ils soient des outils validés établissent des mesures indirectes de notre chronotype et des rythmes veille/sommeil. Le gold standard pour l’évaluation de notre rythmicité circadienne est la mesure biologique de la sécrétion de la mélatonine. Ceci souligne la nécessité d'améliorer les méthodes de diagnostic afin de mieux identifier la prévalence de ces troubles et ainsi pouvoir proposer des mesures thérapeutiques adaptée. Ceci pourrait contribuer à prévenir le développement de pathologies en liens avec ce désalignement circadien. Dans ce contexte, notre travail s’intéresse à la prévalence, les facteurs de risque et les conséquences des désalignements circadiens, chez des populations particulièrement à risque comme les étudiants-sportifs et les patients de soins intensifs, où les donneurs de temps sont perturbés ; générant des conséquences en termes d’altération des performances et du pronostic. Ce travail comprend les résultats de trois études et une revue de littérature sur la prévalence des troubles des rythmes veille/sommeil, leurs facteurs de risque, les conséquences et les traitements potentiels dans les populations des étudiants-sportifs et des patients de soins intensifsSleep health guidelines advocate for 7 to 9 hours of nightly sleep for the general population, yet sleep debt persists, presenting significant health risks, including metabolic, cardiac, mental, and neurocognitive diseases. This widespread sleep debt is often attributed to the conflict between modern lifestyles—characterized by artificial lighting, shift work, and social obligations—and our innate circadian rhythms, leading to a condition known as circadian dysrhythmia. Circadian rhythms are the natural oscillations in physiological processes that are essential for aligning genetic, physiological, and behavioral patterns with solar time to anticipate changes in our environment. The misalignment of these rhythms is increasingly linked to various health disorders. Diagnosing circadian rhythms and sleep/wake disorders poses challenges, as part of its definition relies on subjective assessments and clinical evaluations of sleep quality. Moreover, sleep/wake timing or chronotype questionnaires, although validated, may not accurately reflect individual circadian clocks. While melatonin measurement is considered the gold standard, its practical implementation is difficult, making actigraphy and sleep logs more common tools for identifying circadian rhythms and sleep/wake disorders. This highlights the need for improved diagnostic methods. Potential therapeutic interventions could help improve circadian dysrhythmias related health outcomes. In this context, this manuscript delves into the prevalence, risk factors, and consequences of circadian rhythms and sleep/wake disorders, particularly focusing on at-risk populations like student-athletes and critically ill patients, where misaligned zeitgebers exacerbate health risks. This work includes three studies’ findings and one narrative review on circadian rhythm and sleep/wake disorders, their risk factors, consequences, and potential treatments in populations prioritizing performance (student-athletes) and recovery (critically ill patients)
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Vanbuis, Jade. "Analyse automatique des stades du sommeil à partir des voies électrophysiologiques et cardiorespiratoires." Thesis, Le Mans, 2021. http://cyberdoc-int.univ-lemans.fr/Theses/2021/2021LEMA1004.pdf.
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Le diagnostic des troubles du sommeil repose sur l'analyse de différents signaux enregistrés lors d'un examen du sommeil. Cette analyse est réalisée par un spécialiste du sommeil qui étudie la ventilation et, selon l'outil de diagnostic, la succession des stades de sommeil. Cette dernière tâche est particulièrement chronophage et complexe. Trois algorithmes d'aide au diagnostic et dédiés à cette tâche sont présentés.Le premier permet la classification éveil/sommeil lors de l'utilisation d'un nouvel outil de diagnostic. Il en découle la possibilité pour le médecin de diagnostiquer précisément le syndrome d'apnées du sommeil et à moindre coût.Le deuxième, fondé sur les voies électrophysiologiques, permet d'obtenir une classification de tous les stades de sommeil à partir de l'outil de diagnostic le plus complet. Il a été implémenté en considérant les limitations à l'utilisation d'un tel algorithme en routine clinique. L'architecture de cet algorithme reproduit ainsi le processus de classification réalisé manuellement par les médecins. Une fonction de seuillage auto-adaptatif a aussi été mise en place afin de fournir une classification patient-dépendante. Les résultats obtenus sont comparables avec ceux des médecins.Le troisième algorithme, fondé sur les voies cardio-respiratoires, permet de classifier les stades de sommeil à partir d'un outil de diagnostic très utilisé mais pour lequel il n'est normalement pas possible d'étudier les stades de sommeil. La tâche est complexe, mais les résultats obtenus sont satisfaisants vis-à-vis de la littérature.Les trois algorithmes, destinés aux différents outils de diagnostic, permettront d'aider les spécialistes à analyser le sommeilThe diagnostic of sleep-disordered breathing requires the analysis of various signals obtained while recording sleep. The analysis is carried by a sleep specialist, which studies the patient's ventilation and, depending on the diagnostic tool used for the record, sleep stages. Sleep stage scoring is a complex and time-consuming task. Three diagnosis support algorithms dedicated to this task are presented in this thesis.The first one provides a wakefulness versus sleep classification, designed for a new diagnostic tool. It results in the ability to make a precise diagnosis of sleep apnea syndrome, at low cost.The second algorithm, based on electrophysiological channels, provides a full sleep stage classification while using the most complete diagnosis tool. It was implemented considering the known limitations for the use of algorithms in clinical practice. Its architecture thus reproduces the manual scoring process. A self-adaptative thresholding function was also implemented to provide a patient-dependent classification. The obtained results are comparable with the ones from sleep experts.The third algorithm, based on cardio-respiratory channels, provides a sleep stage classification while using a diagnostic tool that is insufficient for a manual sleep scoring, yet still highly used. The task is challenging but the obtained results are satisfying compared to literature.All three algorithms, which were designed for various diagnostic tools, will help sleep experts analyzing sleep
Books on the topic "Sleep Scoring Algorithms"
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Louis, Rhain Paul. Development and validation of a computer-based sleep-scoring algorithm. Ottawa: National Library of Canada, 2003.
Find full textConference papers on the topic "Sleep Scoring Algorithms"
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WenJie, Li, YaDong Liu, and JinXia Zhou. "A sleep scoring application of ensemble learning algorithms in sleep patient scenario." In Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), edited by Shuangming Yang and Guanglei Wu. SPIE, 2022. http://dx.doi.org/10.1117/12.2647475.
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Huang, Chih-Sheng, Chun-Ling Lin, Li-Wei Ko, Sheng-Yi Liu, Tung-Ping Sua, and Chin-Teng Lin. "A hierarchical classification system for sleep stage scoring via forehead EEG signals." In 2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB). IEEE, 2013. http://dx.doi.org/10.1109/ccmb.2013.6609157.
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Desai, R., T. Ning, and J. Bronzino. "A sleep scoring algorithm for the rat EEG based on AR modeling." In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1988. http://dx.doi.org/10.1109/iembs.1988.94883.
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