Surface electromyography based muscle fatigue progression analysis using modified B distribution time–frequency features. (April 2016)

• Record Type: Journal Article
• Authors: Karthick, P.A.; Ramakrishnan, S.
• Is Part Of: Biomedical signal processing and control. Volume 26(2016)
• Page Start: 42
• Page End: 51
• Publication Date: 2016-04
• Subjects: sEMG -- Time–frequency distribution -- Muscle fatigue progression -- Instantaneous spectral ratio -- Low frequency band -- Medium frequency band -- High frequency band; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.12.007
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: sEMG signals in dynamic contractions exhibits higher degree of nonstationary. Modified B distribution TFD is proposed to address the nonstationary property of sEMG signals. A new approach is used to determine the appropriate kernel parameter for Cohen class TFD. Proposed features are able to track the progression of muscle fatigue. Features derived from MBD TFD found to have lower variability across different subjects. Abstract: In this work, an attempt has been made to analyze the progression of muscle fatigue using surface electromyography (sEMG) signals and modified B distribution (MBD) based time–frequency analysis. For this purpose, signals are recorded from biceps brachii muscles of fifty healthy adult volunteers during dynamic contractions. The recorded signals are preprocessed and then subjected to MBD based time–frequency distribution (TFD). The instantaneous median frequency (IMDF) is extracted from the time–frequency matrix for different values of kernel parameter. The linear regression technique is used to model the temporal variations of IMDF. Correlation coefficient is computed in order to select the appropriate value for kernel parameter of MBD based TFD. Further, extended version of frequency domain features namely instantaneous spectral ratio (InstSPR) at low frequency band (LFB), medium frequency band (MFB) and high frequency band (HFB) are extracted from the time–frequency spectrum. In addition to these features, IMDF and instantaneous mean frequency (IMNF) are also calculated. The least square error based linear regression technique is used to track the slope variations of these features. The results show that MBD based time–frequency spectrum is able to provide the instantaneous variations of frequency components associated with fatiguing contractions. The values of InstSPR at MFB and HFB regions, IMDF and IMNF show a decreasing trend during the progression of muscle fatigue. However, an increasing trend is observed in LFB regions. Further the coefficient of variation is calculated for all the features. It is found that the values of IMDF, IMNF and InstSPR in LFB region have lowest variability across different subjects in comparison with other two features. It appears that this method could be useful in analyzing various neuromuscular activities in normal and abnormal conditions.