Deep Learning Approaches for Cardiac Arrhythmia Detection Using IoT Sensor Data
DOI:
https://doi.org/10.48047/Keywords:
Cardiac arrhythmia, World Health Organization, 1D-CNN, LSTM.Abstract
Cardiac arrhythmia is characterized by irregular heart rhythms and is a significant global health concern, contributing to approximately 31% of total deaths worldwide, as reported by the World Health Organization (WHO). According to the American Heart Association (AHA), one in three deaths in the United States is linked to cardiovascular diseases, surpassing deaths from all types of cancer and chronic respiratory diseases combined. Notably, studies indicate that 2 to 3% of individuals in North America and Europe are affected by atrial fibrillation. Various classifications of cardiac arrhythmia exist, including tachycardia (heart rate above 100 beats per minute), bradycardia (heart rate below 60 beats per minute), premature contractions, and irregular beats known as fibrillation or flutter.
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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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