Time-frequency analysis methods for detecting effects of diabetic neuropathy

There have been several research studies on efficient methods for analysis and classification of electromyography (EMG) signals and adoption of wavelet functions, which is a promising approach for determining the spectral distribution of the signal. This study compares distinct time-frequency analysis methods for investigating the EMG activity of the thigh and calf muscles during gait among non-diabetic subjects and diabetic neuropathic patients. It also attempts to verify, by adaptive optimal kernel and discrete wavelet transform, whether there are EMG alterations related to diabetic neuropathy in the lower limb muscles during gait. The results show that diabetics might not keep up with the mechanical demands of walking by changing muscle fibre recruitment strategies, as seen in the control group. Moreover, principal components analysis indicates more alterations in diabetic motor strategies, and we identify that diabetic subjects need other strategies with different muscle energy production and frequencies to carry out their daily activities.