Deep ECG Mining for Arrhythmia Detection Towards Precision Cardiac Medicine

Abstract

Cardiac disease is one of the prominent reasons of deaths worldwide. The timely de tection of arrhythmias, one of the highly prevalent cardiac abnormalities, is very important and promising for treatment. Electrocardiography (ECG) is well applied to probe the car diac dynamics, nevertheless, it is still challenging to robustly detect the arrhythmia with automatic algorithms, especially when the noise may contaminate the signal to some extent. In this research study, we have not only built and assessed different neural network models to understand their capability in terms of ECE-based arrhythmia detection, but also com prehensively investigated the detection under different kinds of signal-to-noise ratio (SNR). Both Long Short-Term Memory (LSTM) model and Multi-Layer Perception (MLP) model have been developed in the study. Further, we have studied the necessity of fine-tuning of the neural network models, which are pre-trained on other data and demonstrated that it is very important to boost the performance when ECG is contaminated by noise. In the experiments, the LSTM model achieves an accuracy of 99.0%, F1 score of 97.9%, and high precision and recall, with the clean ECE signal. Further, in the high SNR scenario, the LSTM maintains an attractive performance. With the low SNR scenario, though there is some performance drop, the fine-tuning approach helps performance improvement critically. Overall, this study has built the neural network models, and investigated different kinds of signal fidelity including clean, high-SNR, and low-SNR, towards robust arrhythmia detection.