Extracting Long-term Patterns from Electrocardiogram Waveform using Deep Learning

Abstract

Automatic analysis of electrocardiograms (ECG) has been attempted. Although almost all methods pay attention only to short-term waveforms, their changes over time are said often important in diagnosis. In this paper, we focus on such long-term waveform change and propose a method to extract it as a pattern. The proposed method combines a method of expressing time series data as a trajectory in a feature space and feature extraction by an autoencoder. Evaluation experiments suggested the existence of regularity in the pattern and its association with the disease.