Assessment of Pain using Optimized Feature Set from Corrugator EMG

Pain is one of the most complex sensation of human physiology. Till now, physicians use subjective scores for measuring pain of any individual and doctors need to completely depend on patient's response for assessment of pain. Although, these methods are not always effective in the medical field, when the subjects are non-cooperative or unable to response. Hence, subject's response independent pain recognition systems are utmost important. Noxious stimulus excites Sympathetic Nervous System (SNS), which is related to changes in neuro-somatic biosignals and facial expression. In this present work, EMG of corrugator muscle which is pertaining to pain sensitiveness is analyzed. Considering non-linear & non-stationary nature of the EMG signal stimulated through pain, Empirical Mode Decomposition technique is applied on EMG for its data adaptive nature. Taguchi Method of feature optimization is applied onthe IMFs for ranking of features according to their significance. Classification of different nociception levels with `no pain' was performed employing linear SVM algorithm, using all extracted features as well as the most significant features. Appreciable increase in classification accuracy is noticed with optimized set of features.