Real-Time Analysis and Processing of Cardiogram Signals
Тип публікації :
Стаття
Дата випуску :
2021
Автор(и) :
Яворський, Андрій
Мова основного тексту :
Англійська
eKNUTSHIR URL :
Випуск :
1
ISSN :
1812-5409
Початкова сторінка :
108
Кінцева сторінка :
113
Цитування :
[APA 7] Яворський, А. (2021). Real-Time Analysis and Processing of Cardiogram Signals. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics, (1), 108–113. https://doi.org/10.17721/1812-5409.2021/1.14
[ДСТУ] Яворський А. Real-Time Analysis and Processing of Cardiogram Signals. Bulletin of Taras Shevchenko National University of Kyiv. Physics and Mathematics. 2021. no. 1. P. 108—113. DOI: 10.17721/1812-5409.2021/1.14 (date of access: 25.07.2026).
Analysis of Electrocardiogram (ECG) signals is an important task to save and enhance human life because a major cause of death is heart disease and the consequences. In many cases, early diagnostics of such problems can save and prolong life.
In this work, we develop and present an approach to the real-time detection of Atrial Fibrillation (AF) Arrhythmia, which is a common cardiac arrhythmia affecting a large number of people. Being undetected, it develops into chronic disability or even early mortality. At the same time, This disease is hard to diagnose, especially in its early stage. A real-time automatic and non-invasive effective detection is needed to help diagnose this kind of health problem early. In-time medical intervention can save human life. ECG as a record of the heart electrical activity is widely used for detecting different heart disabilities. At the same time, AF is hard to detect due to its non-regular nature, and also because the performance of detection models depends largely on the quality of data and careful feature engineering.
The research is based on the dataset from PhysioNet Computing in Cardiology Challenge 2017. It contains 8528 single-lead ECG recordings of short-term heart rhythms (9-61 sec.). Our method and the trained model reach the known state-of-the-art results in this field, but, at the same time, it is much less computationally intensive, and, thus, less power consumptive to be implemented in an embedded device.
Pages of the article in the issue: 108 - 113
Language of the article: Ukrainian
In this work, we develop and present an approach to the real-time detection of Atrial Fibrillation (AF) Arrhythmia, which is a common cardiac arrhythmia affecting a large number of people. Being undetected, it develops into chronic disability or even early mortality. At the same time, This disease is hard to diagnose, especially in its early stage. A real-time automatic and non-invasive effective detection is needed to help diagnose this kind of health problem early. In-time medical intervention can save human life. ECG as a record of the heart electrical activity is widely used for detecting different heart disabilities. At the same time, AF is hard to detect due to its non-regular nature, and also because the performance of detection models depends largely on the quality of data and careful feature engineering.
The research is based on the dataset from PhysioNet Computing in Cardiology Challenge 2017. It contains 8528 single-lead ECG recordings of short-term heart rhythms (9-61 sec.). Our method and the trained model reach the known state-of-the-art results in this field, but, at the same time, it is much less computationally intensive, and, thus, less power consumptive to be implemented in an embedded device.
Pages of the article in the issue: 108 - 113
Language of the article: Ukrainian
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