ANALYSIS OF SURFACE EMG SIGNALS UNDER FATIGUE AND NON-FATIGUE CONDITIONS USING B-DISTRIBUTION BASED QUADRATIC TIME FREQUENCY DISTRIBUTION

In this paper, an attempt has been made to analyze surface electromyography (sEMG) signals under non-fatigue and fatigue conditions using time-frequency based features. The sEMG signals are recorded from biceps brachii muscle of 50 healthy volunteers under well-defined protocol. The pre-processed signals are divided into six equal epochs. The first and last segments are considered as non-fatigue and fatigue zones respectively. Further, these signals are subjected to B-distribution based quadratic time-frequency distribution (TFD). Time frequency based features such as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted. The expression of spectral entropy is modified to obtain instantaneous spectral entropy (ISPEn) from the time-frequency spectrum. The results show that all the extracted features are distinct in both conditions. It is also observed that the values of all features are higher in non-fatigue zone compared to fatigue condition. It appears that this metho...

[1]  P. A. Karthick,et al.  Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  S. Ramakrishnan,et al.  Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals , 2014, Expert Syst. Appl..

[3]  G. Venugopal,et al.  Analysis of progressive changes associated with muscle fatigue in dynamic contraction of biceps brachii muscle using surface EMG signals and bispectrum features , 2014 .

[4]  Gabriella Olmo,et al.  Matched wavelet approach in stretching analysis of electrically evoked surface EMG signal , 2000, Signal Process..

[5]  Roberto Hornero,et al.  Decreased spectral entropy modulation in patients with schizophrenia during a P300 task , 2014, European Archives of Psychiatry and Clinical Neuroscience.

[6]  M Knaflitz,et al.  Time-frequency methods applied to muscle fatigue assessment during dynamic contractions. , 1999, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[7]  B. Boashash Chapter 6 - Implementation and Realization of TFDs , 2003 .

[8]  Boualem Boashash,et al.  Time-Frequency Signal Analysis and Processing: A Comprehensive Reference , 2015 .

[9]  Paolo Bonato,et al.  Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions , 2001, IEEE Transactions on Biomedical Engineering.

[10]  Braham Barkat,et al.  Performance evaluation of the B-distribution , 1999, ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359).