A Novel Feature Vector for ECG Classification using Deep Learning

Abstract

In the past decade, deep learning techniques have been widely used in the healthcare industry to detect heartbeats and diagnose heart conditions. However, these tools have been criticized for being a “black box” and lacking transparency. Therefore, in this paper, we propose a new approach to making the classification results obtained by deep learning more comprehensible. We suggest forming a vector of features based on ECG signals that correspond to specific heart conditions. This vector includes measurable characteristics of the cardiac cycle, such as wave durations and amplitudes, which are typical and understandable to healthcare professionals. This feature vector serves as input data for a deep neural network that acts as a feature encoder and classifier. Our computational experiments with the handcrafted feature vector achieved an average accuracy of 98.69%, comparable to other deep learning tools based on the complete cardiac cycle. The results of this study suggest that future research should focus on developing interpretable deep learning tools that are transparent and comprehensible to healthcare professionals.