Automatic detection of bruxism and pseudo clenching by EMG and acoustic signals

Abstract

Bruxism is a symptom in which one grinds, gnashes, or clenches one's teeth unconsciously. It may cause the teeth to be chipped, which can result in temporomandibular joint disorder. Pseudo clenching is masseter muscles' excessive tension without tooth contact, which is difficult to distinguish from bruxism. This study was performed to detect bruxism and pseudo clenching automatically by machine learning. Single-stream or multi-stream Hidden Markov Models (HMM) for each of the three classes (bruxism, pseudo clenching, and others) were trained with Mel Frequency Cepstral Coefficients (MFCC) calculated from EMG and acoustic signals from 12 healthy adults. The averaged F measure, calculated with cross-validation, was used to evaluate the model. The F measure with EMG only was 77.2±8.1%, that with acoustics only was 60.1±16.0% and that with both features was 81.7±8.5%.These results indicated that a rather good performance of bruxism detection was realized, but further improvements are needed.