Detection of Bruxism Using Inverse Discrete Wavelet Transformed Reconstructed Band Limited EEG Signals by Group Wise Feature Ranking

Abstract

Bruxism is a sleep disorder which is manifested by unintentional grinding and clenching of teeth during sleep. An automated sleep bruxism recognition system using single channel EEG data is proposed in this paper which is based on Inverse Discrete Wavelet Transformed Reconstructed Band Limited (IDWT-RBL) signals. These band limited EEG signals are used for extracting various features. Instead of using handcrafted features, feature reduction is done by ranking using statistical test scoring combined with classifier testing. This technique finds optimal features, reduces model complexity, lowers computational burden and increases model interpretability. Abundant features from time, frequency and statistical domains are used primarily so that no significant feature is ignored. Choosing a good subset of features from a larger set is a challenge for data with low sample size. To meet this challenge, a Group wise Feature Ranking (GFR) technique is introduced to reduce feature dimension. After statistical ranking and group wise averaging the scores, most significant groups of features are chosen. The proposed scheme is validated on a publicly available dataset. This process is examined for both unlabeled and labeled sleep stage. For segments with unlabeled sleep stage, cubic Support Vector Machine (SVM) performed best for F3C3 channel using 6 features with an accuracy of 97.83%. For segments with labeled sleep stage, F3C3 and REM sleep stage using 10 features performed best with 98.39% accuracy. The accuracy of proposed method is superior to most recent bruxism detection techniques. Finally, the GFR technique is applied to detect sleep disordered breathing (SDB). The outstanding performance to detect SDB clearly demonstrates the versatility of the proposed GFR technique to solve binary classification problem using EEG signals. Moreover, the introduced GFR technique enhances confidence and pellucidity of the system as it is more explainable.

Keywords

• Bruxism
• classification
• EEG signal
• sleep stage
• feature ranking
• feature optimization