Combining the Taguchi Method and Convolutional Neural Networks for Arrhythmia Classification by Using ECG Images with Single Heartbeats

Abstract

In recent years, deep learning has been applied in numerous fields and has yielded excellent results. Convolutional neural networks (CNNs) have been used to analyze electrocardiography (ECG) data in biomedical engineering. This study combines the Taguchi method and CNNs for classifying ECG images from single heartbeats without feature extraction or signal conversion. All of the fifteen types (five classes) in the MIT-BIH Arrhythmia Dataset were included in this study. The classification accuracy achieved 96.79%, which is comparable to the state-of-the-art literature. The proposed model demonstrates effective and efficient performance in the identification of heartbeat diseases while minimizing misdiagnosis.