Artículos de revistas sobre el tema "Sleep Scoring Algorithms"
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Piccini, Jacopo, Elias August, Sami Leon Noel Aziz Hanna, Tiina Siilak y Erna Sif Arnardóttir. "Automatic Detection of Electrodermal Activity Events during Sleep". Signals 4, n.º 4 (18 de diciembre de 2023): 877–91. http://dx.doi.org/10.3390/signals4040048.
Texto completoBiegański, Piotr, Anna Stróż, Marian Dovgialo, Anna Duszyk-Bogorodzka y Piotr Durka. "On the Unification of Common Actigraphic Data Scoring Algorithms". Sensors 21, n.º 18 (21 de septiembre de 2021): 6313. http://dx.doi.org/10.3390/s21186313.
Texto completoKim, Myeong Seok, Tae Kyoung Ha, Ho Dong Lee y Young Jun Lee. "0945 A Robust Hybrid algorithm for automatic respiratory events scoring in adults". SLEEP 46, Supplement_1 (1 de mayo de 2023): A417. http://dx.doi.org/10.1093/sleep/zsad077.0945.
Texto completoChakraborty, Sabyasachi, Satyabrata Aich y 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, n.º 3 (julio de 2021): 1–20. http://dx.doi.org/10.4018/ijsda.2021070101.
Texto completoMagalang, Ulysses, Brendan Keenan, Bethany Staley, Peter Anderer, Marco Ross, Andreas Cerny, Raymond Vasko, Samuel Kuna y Jessie Bakker. "251 Agreement and reliability of a new polysomnography sleep staging algorithm against multiple human scorers". Sleep 44, Supplement_2 (1 de mayo de 2021): A101. http://dx.doi.org/10.1093/sleep/zsab072.250.
Texto completoMagalang, Ulysses, Brendan Keenan, Bethany Staley, Marco Ross, Peter Anderer, Andreas Cerny, Raymond Vasko, Samuel Kuna y Jessie Bakker. "398 Agreement and reliability of a new respiratory event and arousal detection algorithm against multiple human scorers". Sleep 44, Supplement_2 (1 de mayo de 2021): A158. http://dx.doi.org/10.1093/sleep/zsab072.397.
Texto completoHanif, Umaer, Guillaume Jubien, Alyssa Cairns, Tammie Radke y Vincent Mysliwiec. "1078 Performance of USleep Algorithm to a Better Than “Gold-Standard” Polysomnogram Validation Data Set". SLEEP 47, Supplement_1 (20 de abril de 2024): A463. http://dx.doi.org/10.1093/sleep/zsae067.01078.
Texto completoCho, Taeheum, Unang Sunarya, Minsoo Yeo, Bosun Hwang, Yong Seo Koo y Cheolsoo Park. "Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy". Electronics 8, n.º 12 (2 de diciembre de 2019): 1461. http://dx.doi.org/10.3390/electronics8121461.
Texto completoHutchison, Stephen, Michael Grandner, Zohar Bromberg, Zoe Morrell, Arnulf Graf y 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 (25 de mayo de 2022): A45—A46. http://dx.doi.org/10.1093/sleep/zsac079.099.
Texto completoStanus, E., B. Lacroix, M. Kerkhofs y J. Mendlewicz. "Automated sleep scoring: a comparative reliability study of two algorithms". Electroencephalography and Clinical Neurophysiology 66, n.º 4 (abril de 1987): 448–56. http://dx.doi.org/10.1016/0013-4694(87)90214-8.
Texto completoWang, 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 (20 de abril de 2024): A436—A437. http://dx.doi.org/10.1093/sleep/zsae067.01015.
Texto completoToban, Gabriel, Khem Poudel y Don Hong. "REM Sleep Stage Identification with Raw Single-Channel EEG". Bioengineering 10, n.º 9 (11 de septiembre de 2023): 1074. http://dx.doi.org/10.3390/bioengineering10091074.
Texto completoBechny, M., L. Fiorillo, G. Monachino, J. van der Meer, M. H. Schmidt, C. L. A. Bassetti, A. Tzovara y F. D. Faraci. "Do state-of-the-art sleep-scoring algorithms preserve clinical information?" Sleep Medicine 115 (febrero de 2024): 406. http://dx.doi.org/10.1016/j.sleep.2023.11.1091.
Texto completoKuo, C. y G. Chen. "0441 An Automatic Sleep Scoring System Based on Ensemble Convolutional Neural Network and Spectrogram of Sleep Physiological Signal". Sleep 43, Supplement_1 (abril de 2020): A169. http://dx.doi.org/10.1093/sleep/zsaa056.438.
Texto completoAlvarez-Estevez, Diego y Roselyne M. Rijsman. "Inter-database validation of a deep learning approach for automatic sleep scoring". PLOS ONE 16, n.º 8 (16 de agosto de 2021): e0256111. http://dx.doi.org/10.1371/journal.pone.0256111.
Texto completoDimitrov, T. S., M. He y M. J. Prerau. "0449 Characterizing Spindle Activity as a Time-Frequency Phenomenon". Sleep 43, Supplement_1 (abril de 2020): A172. http://dx.doi.org/10.1093/sleep/zsaa056.446.
Texto completoBouchequet, P., D. Leger, M. Lebrun y M. Elbaz. "0442 Experimenting Automatic Sleep Analysis Application in a Clinical Context". Sleep 43, Supplement_1 (abril de 2020): A169—A170. http://dx.doi.org/10.1093/sleep/zsaa056.439.
Texto completoLabrada, Alexei, Elsa Santos Febles y José Manuel Antelo. "Comparison of Automatic Sleep Stage Classification Methods for Clinical Use". Global Clinical Engineering Journal 5, n.º 1 (1 de junio de 2022): 8–17. http://dx.doi.org/10.31354/globalce.v5i1.125.
Texto completoChan, Alexander, Ahmet Cakir, David Josephs, Dave Kleinschmidt, Jay Pathmanathan y Jacob Donoghue. "1079 Robust Automated Sleep Staging Using Only EEG Signals". SLEEP 47, Supplement_1 (20 de abril de 2024): A463—A464. http://dx.doi.org/10.1093/sleep/zsae067.01079.
Texto completoGrassi, Massimiliano, Daniela Caldirola, Silvia Daccò, Giampaolo Perna y Archie Defillo. "253 Better and faster automatic sleep staging with artificial intelligence". Sleep 44, Supplement_2 (1 de mayo de 2021): A102. http://dx.doi.org/10.1093/sleep/zsab072.252.
Texto completoS G, Shashank. "Sleep Disorder Detection Using EEG Signals". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n.º 05 (22 de mayo de 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34611.
Texto completoS, Lydia Pradeepa. "Sleeping Stage Classification Using CNN". International Journal for Research in Applied Science and Engineering Technology 12, n.º 5 (31 de mayo de 2024): 1070–77. http://dx.doi.org/10.22214/ijraset.2024.61348.
Texto completoSayeed, Fahad, Justin Brooks y Nilanjan Banerjee. "0270 Obstructive Sleep Apnea detection using ECG morphology and Machine Learning". SLEEP 46, Supplement_1 (1 de mayo de 2023): A120. http://dx.doi.org/10.1093/sleep/zsad077.0270.
Texto completoHaghayegh, S., S. Khoshnevis, M. H. Smolensky, K. R. Diller y 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 (abril de 2020): A457—A458. http://dx.doi.org/10.1093/sleep/zsaa056.1190.
Texto completoThorey, V., A. Guillot, K. El Kanbi, M. Harris y 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 (abril de 2020): A463. http://dx.doi.org/10.1093/sleep/zsaa056.1205.
Texto completoElMoaqet, Hisham, Jungyoon Kim, Dawn Tilbury, Satya Krishna Ramachandran, Mutaz Ryalat y Chao-Hsien Chu. "Gaussian Mixture Models for Detecting Sleep Apnea Events Using Single Oronasal Airflow Record". Applied Sciences 10, n.º 21 (6 de noviembre de 2020): 7889. http://dx.doi.org/10.3390/app10217889.
Texto completoPeker, Musa. "An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms". Neurocomputing 207 (septiembre de 2016): 165–77. http://dx.doi.org/10.1016/j.neucom.2016.04.049.
Texto completoCoppieters ’t Wallant, Dorothée, Pierre Maquet y 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.
Texto completoYaghouby, Farid, Bruce F. O’Hara y Sridhar Sunderam. "Unsupervised Estimation of Mouse Sleep Scores and Dynamics Using a Graphical Model of Electrophysiological Measurements". International Journal of Neural Systems 26, n.º 04 (27 de abril de 2016): 1650017. http://dx.doi.org/10.1142/s0129065716500179.
Texto completoQuante, Mirja, Emily R. Kaplan, Michael Cailler, Michael Rueschman, Rui Wang, Jia Weng, Elsie M. Taveras y 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 (enero de 2018): 13–20. http://dx.doi.org/10.2147/nss.s151085.
Texto completoGu, Wenbo, Peter Wu, Arthur Liu, Wen-Te Liu, Yi-Chun Kuan, Hsin-Chien Lee, Lydia Leung, I.-Chen Wu y Ambrose Chiang. "0517 Integrating Body Sensor into a Wearable Platform to Enhance the Identification of Central and Mixed Apneas". SLEEP 47, Supplement_1 (20 de abril de 2024): A222. http://dx.doi.org/10.1093/sleep/zsae067.0517.
Texto completoMostafa, Mendonça, Ravelo-García y Morgado-Dias. "A Systematic Review of Detecting Sleep Apnea Using Deep Learning". Sensors 19, n.º 22 (12 de noviembre de 2019): 4934. http://dx.doi.org/10.3390/s19224934.
Texto completoRicketti, P., D. Schwartz, K. Calero, W. Anderson, C. Diaz-Sein, M. Rechkemmer, K. Bell, M. Dahdad y 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 (abril de 2018): A383. http://dx.doi.org/10.1093/sleep/zsy061.1030.
Texto completoKim, Dongyoung, Jeonggun Lee, Yunhee Woo, Jaemin Jeong, Chulho Kim y Dong-Kyu Kim. "Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification". Journal of Personalized Medicine 12, n.º 2 (20 de enero de 2022): 136. http://dx.doi.org/10.3390/jpm12020136.
Texto completoNollet, Mathieu, William Wisden y Nicholas P. Franks. "Sleep deprivation and stress: a reciprocal relationship". Interface Focus 10, n.º 3 (17 de abril de 2020): 20190092. http://dx.doi.org/10.1098/rsfs.2019.0092.
Texto completoHannan, Sana, Alyssa Ho y Birgit Frauscher. "Clinical Utility of Sleep Recordings During Presurgical Epilepsy Evaluation With Stereo-Electroencephalography: A Systematic Review". Journal of Clinical Neurophysiology 41, n.º 5 (julio de 2024): 430–43. http://dx.doi.org/10.1097/wnp.0000000000001057.
Texto completoSuliman, Ahmad, Md Rakibul Mowla, Alaleh Alivar, Charles Carlson, Punit Prakash, Balasubramaniam Natarajan, Steve Warren y 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, n.º 5 (1 de marzo de 2023): 2693. http://dx.doi.org/10.3390/s23052693.
Texto completoKorotun, M., L. Weizman, A. Drori, J. Zaccaria, T. Goldstein, I. Litman, S. Hahn y H. Greenberg. "0584 Detecting Sleep Disordered Breathing Using Sub-Terahertz Radio-Frequency Micro-Radar". Sleep 43, Supplement_1 (abril de 2020): A224. http://dx.doi.org/10.1093/sleep/zsaa056.581.
Texto completoChien, Ying-Ren, Cheng-Hsuan Wu y Hen-Wai Tsao. "Automatic Sleep-Arousal Detection with Single-Lead EEG Using Stacking Ensemble Learning". Sensors 21, n.º 18 (9 de septiembre de 2021): 6049. http://dx.doi.org/10.3390/s21186049.
Texto completoJilani, Shahla M., Chloe J. Jordan, Lauren M. Jansson y Jonathan M. Davis. "Definitions of neonatal abstinence syndrome in clinical studies of mothers and infants: an expert literature review". Journal of Perinatology 41, n.º 6 (29 de enero de 2021): 1364–71. http://dx.doi.org/10.1038/s41372-020-00893-8.
Texto completoThiesse, Laurie, Luc Staner, Patrice Bourgin, Henri Comtet, Gil Fuchs, Debora Kirscher, Thomas Roth, Jean Yves Schaffhauser, Jay B. Saoud y Antoine U. Viola. "Somno-Art Software identifies pathology-induced changes in sleep parameters similarly to polysomnography". PLOS ONE 18, n.º 10 (20 de octubre de 2023): e0291593. http://dx.doi.org/10.1371/journal.pone.0291593.
Texto completoSharma, Manish, Jainendra Tiwari, Virendra Patel y U. Rajendra Acharya. "Automated Identification of Sleep Disorder Types Using Triplet Half-Band Filter and Ensemble Machine Learning Techniques with EEG Signals". Electronics 10, n.º 13 (25 de junio de 2021): 1531. http://dx.doi.org/10.3390/electronics10131531.
Texto completoHorger, Melissa, Allison Swift, Kyle Kainec, Jennifer Holmes y Rebecca Spencer. "0204 Spindle Detection in Polysomnography of Toddlers: Comparing Manual Feature Detection and an Automated Algorithm". SLEEP 47, Supplement_1 (20 de abril de 2024): A88. http://dx.doi.org/10.1093/sleep/zsae067.0204.
Texto completoMcGovney, 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 (abril de 2020): A350—A351. http://dx.doi.org/10.1093/sleep/zsaa056.918.
Texto completoMendonça, Fábio, Sheikh Shanawaz Mostafa, Diogo Freitas, Fernando Morgado-Dias y Antonio G. Ravelo-García. "Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection". Entropy 24, n.º 5 (13 de mayo de 2022): 688. http://dx.doi.org/10.3390/e24050688.
Texto completoStepnowsky, Carl, Tania Zamora, Robert Barker, Lin Liu y 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.
Texto completoGalaska, B., J. M. Bakker, F. Sert Kuniyoshi, M. Bush, J. Salazar, J. G. Jasko, A. L. Friedman y 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 (abril de 2020): A456. http://dx.doi.org/10.1093/sleep/zsaa056.1185.
Texto completoHelland, V. C., A. Gapelyuk, A. Suhrbier, M. Riedl, T. Penzel, J. Kurths y N. Wessel. "Investigation of an Automatic Sleep Stage Classification by Means of Multiscorer Hypnogram". Methods of Information in Medicine 49, n.º 05 (2010): 467–72. http://dx.doi.org/10.3414/me09-02-0052.
Texto completoSvetnik, V., T. Wang, Y. Xu, B. J. Hansen y S. V. Fox. "0432 A Deep Learning Approach for Automated Sleep-Wake Scoring in Pre-Clinical Animal Models". Sleep 43, Supplement_1 (abril de 2020): A166. http://dx.doi.org/10.1093/sleep/zsaa056.429.
Texto completoCho, Jae Hoon, Ji Ho Choi, Ji Eun Moon, Young Jun Lee, Ho Dong Lee y Tae Kyoung Ha. "Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm". Medicina 58, n.º 6 (9 de junio de 2022): 779. http://dx.doi.org/10.3390/medicina58060779.
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