ADVANCED DEEP LEARNING TECHNIQUES FOR THE DETECTION AND CLASSIFICATION OF CARDIAC ARRHYTHMIAS USING IOT ENABLED ECG SENSOR DATA
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https://doi.org/10.48047/Keywords:
Cardiac arrhythmia, World Health Organization, one-dimensional convolutional neural network, long short-term memory.Abstract
Cardiac arrhythmia is a disorder characterized by irregular heart beats. The World Health Organization (WHO) reports that over 17 million individuals globally succumb annually to cardiovascular illnesses. This is around 31% of total worldwide fatalities. The American Heart Association (AHA) reports that one in three fatalities in the United States is attributable to cardiovascular illnesses. Cardiovascular disease fatalities exceed those from all cancer types and chronic lower respiratory disorders combined. A 2014 study reveals that around 2 to 3% of individuals in North America and Europe are impacted by atrial fibrillation. A heart rate above 100 beats per minute in adults is termed tachycardia, whereas a heart rate below 60 beats per minute is referred to as bradycardia.
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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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