RESUMO
The electrocardiogram (ECG) is a vital diagnostic tool used in many health applications. In practice, interference by muscle artifacts is very common and may significantly complicate interpretation of the ECG waveform. In this work we investigate the removal of muscle artifacts in single-channel ECG signals using neural network models. To this end, we compare two neural network architectures which were previously used for ECG denoising and propose a novel third method based on the ConvTasNet. The neural networks are trained on simulated data using artificial mixtures of single-channel ECG (lead II) and surface EMG signals taken from publicly available datasets. ECG data were sampled from the MIT-BIH Arrhythmia and the PTB-XL database. The former provides recordings of ambulatory ECGs while the latter contains a large variety of cardiac pathologies recorded in a clinical setting. The muscle artifacts were sampled from the MIT-BIH Noise Stress Test database and the TaiChi database. In the past, most denoising methods were only tested on the two smaller MIT-BIH datasets. In this work, we report performances on larger datasets and thus provide stronger evidence for a clinical use-case. We also report out-of distribution performance of the three methods by switching the ECG dataset between training and test. The herein investigated variant of the ConvTasNet substantially reduces interference by muscle artifacts, outperforms state-of-the-art methods and thus, may support clinical decision making.