標題: Noise Rejection for Wearable ECGs Using Modeified Frequency Slice Wavelet Transform and Convolutional Neural Networks
作者: Zhao, Zhongyao
Liu, Chengyu
Li, Yaowei
Li, Yixuan
Wang, Jingyu
Lin, Bor-Shyh
Li, Jianqing
影像與生醫光電研究所
Institute of Imaging and Biomedical Photonics
關鍵字: Wearable ECG;signal quality assessment (SQA);convolutional neural network (CNN);modified frequency slice wavelet transform (MFSWT)
公開日期: 1-一月-2019
摘要: 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 200x50. 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.
URI: http://dx.doi.org/10.1109/ACCESS.2019.2900719
http://hdl.handle.net/11536/151670
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2900719
期刊: IEEE ACCESS
Volume: 7
起始頁: 34060
結束頁: 34067
顯示於類別:期刊論文