Frequency Domain Analysis to Identify Neurological Disorders from Evoked EMG Responses

Evoked EMG M-responses obtained from the thenar muscle in the palm by electrical stimulation of the median nerve demonstrate a well-established smooth bipolar shape for normal healthy subjects. Kinks in this curve are observed in certain neurological disorders and preliminary work suggests their relationship to cervical spondylosis. The present work was taken up to develop an objective method for the identification of such neurological disorders for automated diagnosis by analysing the M-responses. A Fourier transform was performed using MATLAB, and features in the frequency domain were studied to distinguish healthy and smooth M-responses from ones with kinks. The features included some basic parameters like peak amplitude, peak frequency, frequency bandwidths, and areas in specified frequency segments. Ratio and deviation parameters from the above basic parameters were also studied to make 39 parameters in all. Out of these 10 came out as 'highly significant', 17 as 'significant' and the rest as insignificant, in statistical t-tests. A weighted combination of the significant parameters may allow identification of kinks with confidence.