Inception based GAN for ECG arrhythmia classification
سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 124
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شناسه ملی سند علمی:
JR_IJNAA-12-0_117
تاریخ نمایه سازی: 11 آذر 1401
چکیده مقاله:
Cardiovascular diseases are the world's principal reason for death, accounting it about ۱۷.۹ 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 ۱۵ 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 ۹۸\%. 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.
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