Onset detection of surface diaphragmatic electromyography based on sample entropy and individualized threshold.
10.7507/1001-5515.201804026
- Author:
Cuilian ZHAO
1
;
Shuangchi MA
2
;
Yexiao LIU
2
Author Information
1. Shanghai Key Lab of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, P.R.China.clzhao@mail.shu.edu.cn.
2. Shanghai Key Lab of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, P.R.China.
- Publication Type:Journal Article
- Keywords:
electrocardiographic interference;
onset detection;
sample entropy;
surface diaphragmatic electromyography
- From:
Journal of Biomedical Engineering
2018;35(6):852-859
- CountryChina
- Language:Chinese
-
Abstract:
The diaphragm is the main respiratory muscle in the body. The onset detection of the surface diaphragmatic electromyography (sEMGdi) can be used in the respiratory rehabilitation training of the hemiparetic stroke patients, but the existence of electrocardiography (ECG) increases the difficulty of onset detection. Therefore, a method based on sample entropy (SampEn) and individualized threshold, referred to as SampEn method, was proposed to detect onset of muscle activity in this paper, which involved the extraction of SampEn features, the optimization of the SampEn parameters and , the selection of individualized threshold and the establishment of the judgment conditions. In this paper, three methods were used to compare onset detection accuracy with the SampEn method, which contained root mean square (RMS) with wavelet transform (WT), Teager-Kaiser energy operator (TKE) with wavelet transform and TKE without wavelet transform, respectively. sEMGdi signals of 12 healthy subjects in 2 different breathing ways were collected for signal synthesis and methods detection. The cumulative sum of the absolute value of error was used as an judgement value to evaluate the accuracy of the four methods. The results show that SampEn method can achieve higher and more stable detection precision than the other three methods, which is an onset detection method that can adapt to individual differences and achieve high detection accuracy without ECG denoising, providing a basis for sEMGdi based respiratory rehabilitation training and real time interaction.
