Using fuzzy logic for diagnosis and classification of spasticity.

Background/aim: 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.

Materials And Methods: 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.

Results: 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.

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