Key result
Novel WTDRL algorithm outperforms traditional and deep learning methods in arrhythmia detection.
Why the study?
Traditional machine learning algorithms disregard temporal aspects and eliminate correlated features during ECG dimensionality reduction, leading to suboptimal arrhythmia detection.
Does the WTDRL method improve arrhythmia detection performance from ECG signals compared to traditional and state-of-the-art deep learning methods?
Does the WTDRL method improve arrhythmia detection performance from ECG signals compared to traditional and state-of-the-art deep learning methods?
A novel deep reinforcement learning approach combining wavelet transform and LSTM improves automated arrhythmia detection from ECG signals.
Hypothesis-generating for automated ECG arrhythmia detection; prospective validation required before clinical use.
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.
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Zhu et al. (2023) studied Arrhythmia. WTDRL (wavelet transform and deep reinforcement learning) vs. Traditional approaches and state-of-the-art deep learning methods was evaluated on Arrhythmia detection performance. A novel method combining wavelet transform and deep reinforcement learning (WTDRL) surpassed traditional and state-of-the-art deep learning approaches in arrhythmia detection performance.
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