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Inception based GAN for ECG arrhythmia classification | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 1585-1594 اصل مقاله (417.29 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.5831 | ||
| نویسندگان | ||
| Neerajkumar S. Sathawane* ؛ Ulhaskumar Gokhale؛ Dinesh Padole؛ Sanjay Wankhede | ||
| GHRCOE, Nagpur, India | ||
| تاریخ دریافت: 17 مرداد 1400، تاریخ بازنگری: 06 آبان 1400، تاریخ پذیرش: 22 آبان 1400 | ||
| چکیده | ||
| Cardiovascular diseases are the world's principal reason for death, accounting it about 17.9 million people per year, as reported by World Health Organization(WHO). Arrhythmia is often a heart disease that is interpreted by a variation in the linearity of the heartbeat. The goal of this study would be to develop a new deep learning technique to accurately interpret arrhythmia utilizing a one-second segment. This paper introduces a novel method for automatic GAN-based arrhythmia classification. The input ECG signal is derived from the fusion of well known Physionet dataset from MIT-BIH and some Hospital ECG databases. The ECG segment over time is used to detect 15 different classes of arrhythmias. The GAN network uses an attention-based generator to learn local essential features and to maintain data integrity for both time and frequency domains. Among these, the highest accuracy obtained is 98\%. It can be inferred from the results that the proposed approach is smart enough to make meaningful predictions and produces excellent performance on the related metrics. | ||
| کلیدواژه‌ها | ||
| Electrocardiogram؛ ECG classification؛ Inception؛ GAN؛ Generative adversarial network | ||
| مراجع | ||
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