Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review
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| Автор |
Lipchak, D. A.
Chupov, A. A. Zhdanov, A. E. Borisov, V. I. |
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| Тематика |
ARRHYTHMIA
AUTOMATIC EXTERNAL DEFIBRILLATOR DEFIBRILLATION DIGITAL SIGNAL PROCESSING FILTERING MACHINE LEARNING BIOMEDICAL SIGNAL PROCESSING COMPUTER AIDED DIAGNOSIS DISEASES E-LEARNING ELECTROCARDIOGRAPHY HEART LEARNING SYSTEMS MACHINE LEARNING SIGNAL ANALYSIS ARRHYTHMIA AUTOMATIC DIAGNOSIS AUTOMATIC EXTERNAL DEFIBRILLATOR CARDIAC ARRHYTHMIA DEFIBRILLATION ECG SIGNALS MACHINE-LEARNING SEMI-AUTOMATICS SIGNALS ANALYSIS VENTRICULAR FIBRILLATION DEFIBRILLATORS |
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| Описание |
Ventricular fibrillation is considered the most common cause of sudden cardiac arrest. Ventricular fibrillation, and ventricular tachycardia often preceding it, are cardiac rhythms that can respond to emergency electroshock therapy and return to normal sinus rhythm when diagnosed early after cardiac arrest with the restoration of adequate cardiac pumping function. However, manually checking ECG signals for the presence of a pattern of such arrhythmias is a risky and time- consuming task in stressful situations and practically impossible in the absence of a qualified medical specialist. Therefore, for the automatic diagnosis of such conditions, systems for the computer classification of arrhythmias to decide on the need for electric cardioversion with the parameters of a high-voltage pulse, calculated adaptively for each patient, are widely used. This paper discusses methods for analyzing the electrocardiographic signal taken from external automatic or semi-automatic defibrillator electrodes to decide the need for defibrillation, which is applicable in the embedded software of automatic, semi-automatic external defibrillators. The paper includes an overview of applicable filtering techniques and subsequent algorithms for extracting, classifying, and compressing features for the ECG signal. Both advantages and disadvantages are discussed for the studied algorithms. © 2022 IEEE.
Russian Foundation for Basic Research, РФФИ, (20-37-90037) The reported study is funded by RFBR according to research project No. 20-37-90037. |
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| Дата |
2024-04-08T11:07:09Z
2024-04-08T11:07:09Z 2022 |
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| Тип |
Conference paper
Conference object (info:eu-repo/semantics/conferenceObject) Published version (info:eu-repo/semantics/publishedVersion) |
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| Идентификатор |
Lipchak, DA, Chupov, AA, Zhdanov, AE & Borisov, VI 2022, Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review. в Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022. Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022, Institute of Electrical and Electronics Engineers Inc., стр. 8-11, 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022, Yekaterinburg, Российская Федерация, 19/09/2022. https://doi.org/10.1109/USBEREIT56278.2022.9923383
Lipchak, D. A., Chupov, A. A., Zhdanov, A. E., & Borisov, V. I. (2022). Methods of Signal Analysis for Automatic Diagnosis of Shockable Cardiac Arrhythmias: A Review. в Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022 (стр. 8-11). (Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/USBEREIT56278.2022.9923383 978-166546092-7 Final All Open Access; Green Open Access https://journals.urfu.ru/index.php/urj/article/download/5730/pdf https://journals.urfu.ru/index.php/urj/article/download/5730/pdf http://elar.urfu.ru/handle/10995/131414 10.1109/USBEREIT56278.2022.9923383 85141848678 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
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| Формат |
application/pdf
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| Издатель |
Institute of Electrical and Electronics Engineers Inc.
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| Источник |
2022 Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT)
Proceedings - 2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022 |
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