Meng Chen
ABSTRACT
Abstract: Motion interference is a major issue in the process of acquiring electrocardiogram (ECG) signals. It introduces noise and artifacts into the ECG signal, causing signal distortion and deformation, thus affecting the accuracy of subsequent analyses and diagnoses. This study introduces a deep learning-based model for motion artifact removal in ECG signals, namely MSCT, which combines Multi-Scale Convolution with Transformer encoder to extract the local and global features of ECG signals and motion artifacts. The comparative experiments are conducted on the MIT-BIH arrythmia database which is contaminated with the motion artifact from MIT-BIH noise stress test database. Our MSCT model shows significantly higher Cosine Similarity and output signal-to-noise ratio (SNR), and lower Root Mean Square Error (RMSE) and Multi-Scale Entropy Based Weighted Percentage Root-mean-square Difference (MSEWPRD), compared with that of the three previous methods on the motion artifact-corrupted data with various input SNR (-6 dB, 0 dB and 6 dB). Especially, when the input SNR is -6 dB, the proposed MSCT achieves the results 6.15±2.83%, 0.25±0.14 mV, 0.91±0.08, 9.20±3.74 dB for MSEWPRD, RMSE, Cosine Similarity and SNR, respectively. All the four metrics, including quantitative and qualitative metrics, have demonstrated the superiority and robust of the MSCT model proposed in this study. Furthermore, the time-domain and frequency-domain plots of a randomly selected denoising segment at -6 dB effectively showcase the superior performance of our denoising method.
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Index Terms
- Multiscale Convolution and Attention based Denoising Autoencoder for Motion Artifact Removal in ECG Signals
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