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01 January 2005

Nonlinear analysis of electromyography time series as a diagnostic tool for low back pain

Paul S. Sung, Ulrich Zurcher, Miron Kaufman

Med Sci Monit 2005; 11(1): CS1-5 :: ID: 13880


Background: A number of studies have evaluated lumbar spinal muscle fatigue using the electromyography (EMG) signal. However, back muscle fatigue studies do not consistently report endurance levels for patients with or without low back pain (LBP). In this case report, we investigated a nonlinear analysis of EMG time series that characterizes their complexity.Material/Methods: A 37-year-old male with chronic LBP and an age- and gender-matched volunteer were compared.The endurance of the erector spinae muscle was determined using a modifi ed version of the isometric Sorensen fatigue test. Nonlinear time series analysis techniques reveal the presence of longrange,power-law correlations. After checking that the signal was stationary, the original time series of 60,000 entries was reduced to 6,000 entries by averaging over 10 consecutive entries.Results: There was a difference between the entropy time dependence exhibited by entropy time dependence exhibited by the healthy subject and the LBP subject. The entropy associated with the LBP subject saturates at very short-times – two orders of magnitude shorter than for the healthy subject.Conclusions: The characterization of the nonlinear time series in this case study provides a consistent measure of back muscle activities. It is important to understand the potential limitations before undertaking an EMG analysis in the fi eld of ergonomics or biomechanics. Further studies are needed to investigate the characteristics of back muscles.

Keywords: Electromyography, Low Back Pain - physiopathology, Lumbosacral Region, Muscle Fatigue, Muscle, Skeletal - physiopathology, Nonlinear Dynamics, Electromyography, Low Back Pain - physiopathology, Lumbosacral Region, Muscle Fatigue, Muscle, Skeletal - physiopathology, Nonlinear Dynamics

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750