UBC Theses and Dissertations
Pattern recognition using surface electromyography of the anterior temporalis and masseter muscles Long, Christopher L.
Many factors are thought to be involved in the dynamic interplay, or equilibrium between the different components of the masticatory system. Studies have attempted to analyse the normal functional, and parafunctional behaviour of the masticatory muscles. The most widely used treatment mode for parafunctional behaviours is an intra-oral occlusal apppliance (or splint), and the mechanism of action of intra-oral splints remains controversial. There has been no attempt to link muscle activity pattern recognition with the jaw movements produced, or for that matter with the resultant forces developed. Neither has a satisfactory method for pattern recognition been proposed to analyse jaw movement patterns. The aim of this study was to develop a pattern recognition system capable of predicting forceful movements of the jaw using an occlusal appliance, and to develop an analytical methodology for discriminating the features of EMG recordings of the 4 muscles relative to specified intra-oral tasks. The experiments were divided into three main studies: A reproducibility study , in which a subject, using an occlusal splint and performing a series of prescribed movements was recorded using EMG of the anterior temporalis, and masseter muscles bilaterally. This showed that it was possible to identify patterns on a daily basis, and that it was possible to discriminate between different movement directions more reliably than different movement speeds. A pattern recognition study was performed utilizing the previous results, and showed that for the same subject it was possible to predict the movements 98.2% of the time for the 5 day period. The final study involved the pattern recognition for a sample group of 10 subjects, this resulted in a 95.7% success rate overall for movement prediction. This study has shown that using a relatively simple computer algorithm, the smoothed and filtered EMG waveform, and discriminant analysis, it is possible to discriminate between different simulated bruxist-like movements.
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