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Bruxism is known as the rhythmical clenching of the lower jaw (mandibular) by the contraction of the masticatory muscles and parafunctional grinding of the teeth. It affects patients’ quality of life adversely due to tooth wear, pain, and fatigue in the jaw muscles. Recently, effective diagnosis methods that use electromyography, electrocardiography, and electroencephalography have been developed for bruxism. However, these methods are not economical since they require specialization and can be performed in clinical conditions. Although using surface electromyography signals alone is an economical solution, it is difficult to identify fatigue and parafunctional movements of the jaw muscles via electromyography signals due to peripheral effects. In this study, to achieve an accurate diagnosis of bruxism economically with only electromyography measurements, a new approach based on Autoregression and Shannon Entropies of Discrete Wavelet Transform Energy Spectra to identify jaw muscle activities and fatigue conditions is proposed. By using Artificial Neural Networks in the proposed model, bruxism activities can be detected most accurately.

Detection of EMG Signals by Neural Networks Using Autoregression and Wavelet Entropy for Bruxism Diagnosis