title: Bioelectric signal detrending using smoothness prior approach.
abstract: Bioelectric signals such as electromyogram (EMG) and electrocardiogram (ECG) are often affected by various low-frequency trending interferences. It is critical to remove these interferences from the recordings so that the critical features of the bioelectric signals could be clearly observed. In this study, an advanced method based on smoothness prior approach (SPA) was proposed to solve this problem. EMG and ECG signals from both the MIT-BIH database and the experiments were employed to evaluate the detrending performance of the proposed method. For comparison purposes, a conventional high-pass Butterworth filter was also used for the detrending of the EMG and ECG signals. Two numerical measures, the correlation coefficient (CC) and root mean square error (RMSE) between the clean data and the detrended data, were calculated to evaluate the detrending performance. The results showed that the proposed SPA method outperformed the high-pass filtering method in reducing various kinds of trending interferences and preserving the desired frequency contents of the EMG and ECG signals. The study suggested that the SPA method might be a promising approach in detrending bioelectric signals.
authors.affiliation: Institute of Biomedical and Health Engineering and the Key Lab of Human-Machine-Intelligence Synergic System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Institute of Biomedical and Health Engineering and the Key Lab of Human-Machine-Intelligence Synergic System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
authors.forename: Shixiong
authors.initials: S
authors.lastname: Chen
authors.affiliation: Institute of Biomedical and Health Engineering and the Key Lab of Human-Machine-Intelligence Synergic System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
authors.forename: Haoshi
authors.initials: H
authors.lastname: Zhang
authors.affiliation: The National Research Center for Rehabilitation Technical Aids, Beijing, China.
authors.forename: Xiufeng
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Institute of Biomedical and Health Engineering and the Key Lab of Human-Machine-Intelligence Synergic System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China. Electronic address: gl.li@siat.ac.cn.
authors.forename: Guanglin
authors.initials: G
authors.lastname: Li
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D016208:Databases, Factual; D004562:Electrocardiography / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D005542:Forearm / Q000502:physiology; D006801:Humans; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology; D012815:Signal Processing, Computer-Assisted
keywords: Bioelectric signal detrending; High-pass filter; Smoothness prior approach; Trending interferences