Arrhythmia Detection from Electrocardiogram Signal Data Based on Wavelet Transform and Deep Reinforcement Learning

Abstract

The analysis of electrocardiogram (ECG) signals holds the potential to predict arrhythmia and prevent cardiovas-cular disease. However, ECG data possess high dimensionality, high noise, and exhibit time sequence properties. Traditional machine learning algorithms often eliminate many highly correlated features during dimensionality reduction, thereby disregarding the temporal aspect of the data and resulting in subpar detection performance. In this paper, we propose a novel arrhythmia detection method from ECG signals, named WTDRL, which combines wavelet transform and deep reinforcement learning. Specifically, the proposed method employs a discrete wavelet transform to decompose the signal into sub-signals characterized by distinct frequency ranges. By subsequently applying suitable filtering and thresholding techniques to each sub-signal, the presence of noise can be effectively mitigated. Furthermore, WTDRL leverages a reinforcement learning model with the deep deterministic strategy algorithm and long short-term memory (LSTM) to determine the optimal strategy for predicting the heartbeat type of subjects and effectively detecting arrhythmia. Experimental results on a public ECG dataset demonstrate that the proposed method surpasses traditional approaches and some state-of-the-art methods of deep learning in terms of arrhythmia detection performance.