Using fuzzy logic for diagnosis and classification of spasticity

Spasticity is generally defined as a sensory-motor control disorder. However, there is no pathophysiological mechanism or appropriate measurement and evaluation standards that can explain all aspects of a possible spasticity occurrence. The objective of this study is to develop a fuzzy logic classifier (FLC) diagnosis system, in which a quantitative evaluation is performed by surface electromyography (EMG), and investigate underlying pathophysiological mechanisms of spasticity.

Surface EMG signals recorded from the tibialis anterior and medial gastrocnemius muscles of hemiplegic patients with spasticity and a healthy control group were analyzed in standing, resting, dorsal flexion, and plantar flexion positions. The signals were processed with different methods: by using their amplitudes in the time domain, by applying short-time Fourier transform, and by applying wavelet transform. A Mamdani-type multiple-input, single-output FLC with 64 rules was developed to analyze EMG signals.

The wavelet transform provided better positive findings among all three methods used in this study. The FLC test results showed that the test was 100% sensitive to identify spasticity with 95.8% accuracy and 93.8% specificity.

A FLC was successfully designed to detect and identify spasticity in spite of existing measurement difficulties in its nature.