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Название: Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation
Авторы: Markov, N.
Kotov, I.
Ushenin, K.
Bozhko, Y.
Дата публикации: 2022
Издатель: Institute of Electrical and Electronics Engineers Inc.
Библиографическое описание: Markov, N, Kotov, I, Ushenin, K & Bozhko, Y 2022, Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation. в Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022. Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022, Institute of Electrical and Electronics Engineers Inc., стр. 130-133. https://doi.org/10.1109/CSGB56354.2022.9865298
Markov, N., Kotov, I., Ushenin, K., & Bozhko, Y. (2022). Statistical model for describing heart rate variability in normal rhythm and atrial fibrillation. в Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022 (стр. 130-133). (Proceedings - 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CSGB56354.2022.9865298
Аннотация: Heart rate variability (HRV) indices describe properties of interbeat intervals in electrocardiogram (ECG). Usually HRV is measured exclusively in normal sinus rhythm (NSR) excluding any form of paroxysmal rhythm. Atrial fibrillation (AF) is the most widespread cardiac arrhythmia in human population. Usually such abnormal rhythm is not analyzed and assumed to be chaotic and unpredictable. Nonetheless, ranges of HRV indices differ between patients with AF, yet physiological characteristics which influence them are poorly understood. In this study, we propose a statistical model that describes relationship between HRV indices in NSR and AF. The model is based on Mahalanobis distance, the k-Nearest neighbour approach and multivariate normal distribution framework. Verification of the method was performed using 10 min intervals of NSR and AF that were extracted from long-term Holter ECGs. For validation we used Bhattacharyya distance and Kolmogorov-Smirnov 2-sample test in a k-fold procedure. The model is able to predict at least 7 HRV indices with high precision. © 2022 IEEE.
Ключевые слова: ATRIAL FIBRILLATION
HEART RATE VARIABILITY
MACHINE LEARNING
STATISTICAL MODEL
CARDIOLOGY
COMPUTATIONAL COMPLEXITY
DISEASES
ELECTROCARDIOGRAMS
HEART
NEAREST NEIGHBOR SEARCH
NORMAL DISTRIBUTION
ATRIAL FIBRILLATION
CARDIAC ARRHYTHMIA
CHAOTICS
HEART RATE VARIABILITY
HUMAN POPULATION
MACHINE-LEARNING
NORMAL SINUS RHYTHM
PROPERTY
STATISTIC MODELING
VARIABILITY INDEX
MACHINE LEARNING
URI: http://elar.urfu.ru/handle/10995/131415
Условия доступа: info:eu-repo/semantics/openAccess
Конференция/семинар: 7 July 2022 through 8 July 2022
Дата конференции/семинара: 2022 Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine, CSGB 2022
Идентификатор SCOPUS: 85138464136
Идентификатор PURE: 30979506
f7edfa52-cc95-403f-b647-437361f14b91
ISBN: 978-166545288-5
DOI: 10.1109/CSGB56354.2022.9865298
Располагается в коллекциях:Научные публикации ученых УрФУ, проиндексированные в SCOPUS и WoS CC

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