Sleep Bruxism Disorder Detection and Feature Extraction Using Discrete Wavelet Transform

Abstract

Sleep Bruxism is characterized by an unconscious act of tooth grinding or clenching of teeth tightly during sleep or awake state. Early diagnosis is advantageous to overcome the damage of jaw, damage of teeth and other health related problems. This paper focuses clenching of teeth in the sleep state only. This paper presents sleep bruxism disease detection and feature extraction. The electroencephalogram (EEG) signal analysis is one of the useful methods for detecting sleep bruxism disorder. For this analysis 10 subjects are considered. For these 10 subjects the EEG signal is extracted from frontal and temporal electrodes F7–T3, T3–T5 and T4–T6. These EEG signals are decomposed into five sub-bands D6-gamma, D7-beta, D8-alpha, D9-theta and A9-delta. The decomposition is done in nine levels since the signals considered has a sampling frequency of 512 Hz. The signal is decomposed using Daubechies order 2 wavelet. From the decomposed signals the detailed coefficients (D1 to D9) and approximation coefficient (A9) are extracted. From extracted coefficient features like energy, variance, mean and standard deviation are calculated to detect sleep bruxism disorder.