Advanced Deep Learning Models for Real-Time Detection of Cardiac Arrhythmias Using ECG Data
DOI:
https://doi.org/10.48047/Keywords:
Cardiac arrhythmia, deep learning, ECG signals.Abstract
Cardiac arrhythmia, characterized by irregular heart rhythms, poses a significant global health risk, contributing to approximately 17 million deaths annually due to cardiovascular diseases, according to the World Health Organization (WHO). In the United States, the American Heart Association (AHA) reports that one in three deaths is linked to these conditions, surpassing fatalities from all forms of
cancer and chronic lower respiratory diseases combined. A 2014 study indicates that about 2 to 3% of individuals in North America and Europe are affected by atrial fibrillation. Classifications of cardiac arrhythmia include tachycardia (heart rate exceeding 100 beats per minute), bradycardia (heart rate below 60 beats per minute), premature contractions, and irregular beats known as fibrillation or flutter. Additionally, arrhythmias can be classified based on the site of origin of the irregular heart rate.
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References
N Jafarnia, Dabanloo, G Attarodi. “A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal”. 2014/01/01. Pp 818-824, vol.7, Journal of Biomedical Science and Engineering.
L. N. Sharma, R. K. Tripathy, and S. Dandapat, "Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction," in IEEE Transactions on Biomedical Engineering, vol. 62, no. 7, pp. 1827-1837, July 2015, doi: 10.1109/TBME.2015.2405134.
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