An LSTM and GAN Based ECG Abnormal Signal Generator

• Published in: Advances in Artificial Intelligence and Applied Cognitive Computing pp. 743 - 755
• Main Authors: Sun, Han, Zhang, Fan, Zhang, Yunxiang
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2021
• Series: Transactions on Computational Science and Computational Intelligence
• Subjects: ECG arrhythmia classification; GANs; LSTM
• ISBN: 9783030702953; 3030702952
• ISSN: 2569-7072; 2569-7080
• DOI: 10.1007/978-3-030-70296-0_54

The electrocardiogram (ECG), a recording of the electrical activity of the heart, is commonly used for cardiac analysis, but lack of abnormal ECG signal data restricts the development of high quality automatic auxiliary diagnosis. In this paper, we introduce an LSTM and GAN based ECG abnormal signal generator to alleviate the issue. By training with a small set of real abnormal signals, the proposed generator can learn and produce high quality fake abnormal signals. The fake signals are then combined with real signals to train abnormal ECG classifiers. We show that our method can significantly improve the ability of classifiers in recognizing the uncommon case with a low proportion in the database.