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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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Файл | Описание | Размер | Формат | |
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2-s2.0-85138464136.pdf | 292,52 kB | Adobe PDF | Просмотреть/Открыть |
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