Key result
A self-supervised neural network ECG de-noising method outperformed traditional de-noising methods in ECG signal classification on a public dataset.
Why the study?
Existing ECG de-noising approaches often fail to consider the specific data structure of ECG signals and are not adaptive across diverse recording devices or skin electrodes.
Does a self-supervised neural network method improve ECG de-noising and subsequent classification compared to traditional methods?
Does a self-supervised neural network method improve ECG de-noising and subsequent classification compared to traditional methods?
A novel self-supervised neural network approach for ECG de-noising improves signal classification accuracy compared to traditional methods.
Should not yet change ECG de-noising practice; hypothesis-generating for self-supervised neural networks in arrhythmia classification.
The electrocardiogram (ECG) records heartbeats and is potentially life-saving. However, the ECG signals (e.g., recorded by the standard ECG monitoring system or the Holter ECG monitoring system) heavily suffered from the noises. Thus, the recorded signals involve meaningful cardiac deflections, other biological waves (e.g., caused by the muscle or electrode movements), and even some noises from the monitoring devices (e.g., the power cables). These noise sources would result in inaccurate analyses of cardiac diseases, thus requiring the ECG de-noising methods in data pre-processing phase before the diagnoses. Previous work provided various ECG de-noising approaches, typically based on some filter algorithms or some wave decomposition algorithms. Most of these approaches did not profoundly consider the ECG signals’ specific data structure, and were not adaptive to the signals recorded by various devices or different skin electrodes. Inspired by the ECG recording theory, we find it available to extract noise information from noisy ECG signals directly. We propose a new ECG de-noising method implemented by the neural network, which de-noise the ECG signals without the supervision of the clean signals. The procedure in the self-supervision is straightforward: we first estimate and simulate the noise signals according to the given noisy ECG signals, and then “subtract” the simulated noises to obtain the de-noised ECG signals by the neural network. Experiments on a public dataset verify that our approach is adaptive to ECG signals from different patients and devices. Also, it is proven that the classification on the ECG signals de-noised by the proposed de-noising methods outperforms those with the traditional de-noising methods.
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Huang et al. (2025) studied ECG noise. Self-supervised neural network ECG de-noising method vs. Traditional de-noising methods was evaluated on ECG signal classification performance. A self-supervised neural network ECG de-noising method outperformed traditional de-noising methods in ECG signal classification on a public dataset.
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