Key result
A noise rejection method combining modified frequency slice wavelet transform and a convolutional neural network achieved 86.3% classification accuracy for wearable ECG segments.
Why the study?
Long-term wearable ECG monitoring is often contaminated by noises affecting cardiovascular disease diagnosis, creating a need for automatic noise rejection in large wearable datasets.
A novel noise rejection method combining MFSWT and CNN achieved 86.3% accuracy in classifying wearable ECG signal quality, outperforming traditional methods.
May aid wearable ECG monitoring; leaves open prospective clinical validation before adoption.
Progress in wearable techniques makes the long-term daily electrocardiogram (ECG) monitoring possible. However, the long-term wearable ECGs can be significantly contaminated by various noises, which affect the detection and diagnosis of cardiovascular diseases (CVDs). The situation becomes more serious for wearable ECG screening, where the data are huge, and doctors have no way to visually check the signal quality episode-by-episode. Therefore, automatic and accurate noise rejection for the wearable big-data ECGs is craving. This paper addressed this issue and proposed a noise rejection method for wearable ECGs based on the combination of modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN). Wearable ECGs were recorded using the newly developed 12-lead Lenovo smart ECG vest with a sample rate of 500 Hz and a resolution of 16 bit. One thousand 10-s ECG segments were picked up and were manually labeled into three quality types: clinically useful segments with good signal quality (type A), clinically useful segments with poor signal quality (type B), and clinically useless segments (pure noises, type C). Each of the 1,000 10-s ECG segments were transformed into a 2-D time-frequency (T-F) image using the MFSWT, with a pixel size of 200×50. Then, the 2-D grayscale images from MFSWT were fed into a 13-layer CNN model for training the classification models. Results from the standard 5-folder cross-validation showed that the proposed combination method of MFSWT and CNN achieved a highest classification accuracy of 86.3%, which was higher than the comparable methods from continuous wavelet transform (CWT) and artificial neural networks (ANN). The combination of MFSWT and CNN also had a good calculation efficiency. This paper indicated that the combination of MFSWT and CNN is a potential method for automatic identification of noisy segments from wearable ECG recordings.
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Zhao et al. (2019) studied Wearable ECG monitoring. Modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN) vs. Continuous wavelet transform (CWT) and artificial neural networks (ANN) was evaluated on Classification accuracy. A noise rejection method combining modified frequency slice wavelet transform and a convolutional neural network achieved 86.3% classification accuracy for wearable ECG segments.
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