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Статті в журналах з теми "Sleep Scoring Algorithms"
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.
Повний текст джерелаBiegański, 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.
Повний текст джерелаKim, 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.
Повний текст джерелаChakraborty, 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.
Повний текст джерелаMagalang, 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.
Повний текст джерелаMagalang, 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.
Повний текст джерелаHanif, 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.
Повний текст джерелаCho, 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.
Повний текст джерелаHutchison, 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.
Повний текст джерелаStanus, 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.
Повний текст джерелаДисертації з теми "Sleep Scoring Algorithms"
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.
Повний текст джерелаSömnen 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.
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.
Повний текст джерелаSleep 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)
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.
Повний текст джерелаThe 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
Книги з теми "Sleep Scoring Algorithms"
Louis, Rhain Paul. Development and validation of a computer-based sleep-scoring algorithm. Ottawa: National Library of Canada, 2003.
Знайти повний текст джерелаТези доповідей конференцій з теми "Sleep Scoring Algorithms"
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.
Повний текст джерелаHuang, 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.
Повний текст джерелаDesai, 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.
Повний текст джерелаDrouot, X., C. Rault, Q. Heraud, J. P. Frat, and A. Thille. "A Real-time Automated Sleep Scoring Algorithm to Detect Refreshing Sleep in Conscious Ventilated Critically Ill Patients." In American Thoracic Society 2023 International Conference, May 19-24, 2023 - Washington, DC. American Thoracic Society, 2023. http://dx.doi.org/10.1164/ajrccm-conference.2023.207.1_meetingabstracts.a6198.
Повний текст джерелаBakker, J. P., M. Ross, R. Vasko, A. Cerny, J. Jasko, E. Shaw, D. P. White, and P. Anderer. "Validation of a New Auto-Scoring Algorithm Against Human Scoring of Respiratory Events, Arousals, Periodic Limb Movements, and Sleep Staging." In American Thoracic Society 2021 International Conference, May 14-19, 2021 - San Diego, CA. American Thoracic Society, 2021. http://dx.doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a4704.
Повний текст джерелаBakker, J. P., M. Ross, R. Vasko, A. Cerny, P. Fonseca, J. Jasko, E. Shaw, D. P. White, and P. Anderer. "Validation of a New Auto-Scoring Algorithm to Estimate Sleep Staging Using Cardiorespiratory Signals." In American Thoracic Society 2021 International Conference, May 14-19, 2021 - San Diego, CA. American Thoracic Society, 2021. http://dx.doi.org/10.1164/ajrccm-conference.2021.203.1_meetingabstracts.a4702.
Повний текст джерелаGonzalez Muñoz, I., J. Solorzano Egurbide, L. Cortezon Garces, N. Ortiz Laza, C. Valverde Novillo, S. Castro Quintas, A. Urrutia Gajate, B. Gonzalez Quero, and V. Cabriada Nuño. "Comparative study between Nox Body Sleep algorithm (NBS) and the manual scoring (MS) of respiratory polygraphs (RP)." In ERS International Congress 2022 abstracts. European Respiratory Society, 2022. http://dx.doi.org/10.1183/13993003.congress-2022.411.
Повний текст джерела