Onset detection of surface diaphragmatic electromyography based on sample entropy and individualized threshold / 生物医学工程学杂志

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.

Motion signal extraction method based on sEMG energy Gauss distribution characteristics / 中国医疗器械杂志

Motion segment and extraction from continuous signals is the premise of surface electromyography (sEMG) analysis. For the problem that sEMG energy threshold required repeated manual testing to set, this the paper established a this mathematical model of continuous actions based on Gaussian sEMG energy curve, in which the energy threshold was set according to the distribution of Gaussian signal section, and differentiated the action signals from no-action signals combined with energy comparison method. The experiment results showed the method can achieve continuous repetitive action segmentation, and compared with manual segmentation of the same signal, has a higher degree of coincidence.