Strategy for Electromyography Based Diagnosis of Neuromuscular Diseases for Assistive Rehabilitation

Abstract

Assistive Rehabilitation aims at developing procedures and therapies which reinstate lost body functions for individuals with disabilities. Researchers have monitored electrophysiological activity of muscles using biofeedback obtained from Electromyogram signals collected at appropriate innervation points. In this paper, we present a comprehensive technique for detection of neuromuscular disease in a subject and a strategy for continuous therapeutic assessment using the Rehabilitation Assessment Matrix. The decision making tool has been trained using a wide spectrum of synthetic physiological data incorporating varying degrees of myopathy and neuropathy from beginning stages to acute. The statistical, spectral and cepstral features extracted from EMG have been used to train a Cascade Correlation Neural Network Classifier for disease assessment. The diagnostic yield of the classifier is 91.2% accuracy, 85.3% specificity and 91.35% sensitivity. The strategy has also been extended to include isotonic contractions in addition to static isometric contractions. This comprehensive strategy is proposed to aid physicians plan and schedule treatment procedures to maximize the therapeutic value of the rehabilitation process.