Autoregressive Modeling of Surface EMG and Its Spectrum with Application to Fatigue

• Published in: IEEE transactions on biomedical engineering Vol. BME-34; no. 10; pp. 761 - 770
• Main Authors: Paiss, Omry, Inbar, Gideon F.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.1987; Institute of Electrical and Electronics Engineers
• Subjects: Acoustic reflection; Biological and medical sciences; Biomedical electrodes; Computerized, statistical medical data processing and models in biomedicine; Electromyography; Fatigue; Fatigue - physiopathology; Frequency domain analysis; Humans; Medical sciences; Models and simulation; Models, Biological; Muscles; Signal processing; Signal Processing, Computer-Assisted; Stochastic processes; Surface morphology; White noise; Fatigue; Action potential; Electromyography; Autoregressive model; Mathematical model; Spectrum; Surface
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.1987.325918

The following is an investigation of the ability of the autoregressive (AR) model to describe the spectrum of the processes underlying the recorded surface EMG. Surface EMG (SEMG) spectrum is influenced by two major factors; one attributed to the motor units (MU) firing rate and the second, the higher frequency one, to the morphology of the action potentials (AP) traveling along the muscle fiber. In the present paper, SEMG measurements were carried out on the biceps brachii muscle with fixed surface electrodes arrangement and isotonic conditions. Sufficient averaging of 0.5 s segments enabled the identification of the low-frequency peak related to the firing rates of the MU's.