Interpretable Electrocardiogram Mapping to Detect Decreased Cardiac Contraction

Left ventricular ejection fraction (EF) is an important indicator of echocardiography for heart disease. Because echocardiography is costly and time-consuming, there is a need for a simpler method to predict a low EF in clinical practice. In recent years, deep neural network (DNN)-based models have been used to predict low EF with high accuracy based on electrocardiography (ECG), which is easy to perform. However, DNN-based models are incomprehensive for clinicians, and lack of interpretability is one of the biggest barriers to their use. In this paper, two new methods are proposed; one is a pre-process method, and the other is an analysis method. The pre-process method extracts one heartbeat of a fixed size from ECG; therefore, it enables many traditional machine learning approaches to be applied to ECG data. The analysis method involves interpretable and unsupervised mapping of ECG using the pre-process method and reveals that one heartbeat on ECG holds information on a low EF upon numerical evaluation. The findings of an inverse analysis corresponded to previous clinical research, which suggests that the proposed method is reliable.