2D Transfer Learning for ECG Classification using Continuous Wavelet Transform

Abstract

Advanced deep neural networks, when trained on extensive datasets, can outperform cardiologists in diagnosing cardiac arrhythmias. However, the availability of large-scale training data is often impractical. This study explores the use of transfer learning to identify and classify three ECG patterns. It applies knowledge gained from 2D image classification tasks to the domain of 1D time-series ECG signal classification. The research leverages various deep learning models to classify continuous wavelet transform (2D representations) of ECG signals. The effectiveness of these transferred deep learning models in classifying ECG time-series data is then evaluated.