Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG

Md Belal Bin Heyat,Faez Iqbal Khan,Yifei Zhang,Dakun Lai

doi:10.1109/access.2019.2928020

Abstract

Lack of sleep causes many sleep disorders such as nocturnal frontal lobe epilepsy, narcolepsy, bruxism, sleep apnea, insomnia, periodic limb movement disorder, and rapid eye movement behavioral disorder. Out of all, bruxism is a common behavior, which is found in 8-31% of the population. Bruxism is a sleep disorder in which individuals involuntarily grinds and clenches the teeth. The main aim of this work is to detect sleep bruxism by analyzing the electroencephalogram (EEG) spectrum analysis of the change in the domain of different stages of sleep. The present research was performed in different stages such as collection of the data, preprocessing of the EEG signal, analysis of the C4-P4 and C4-A1 channels, comparison between healthy humans and bruxism patients, and classification using decision tree method. In this study, the channels C4-P4 and C4-A1 of the EEG signal were combined for the detection of bruxism by using Welch technique, which mainly focused on two sleep stages such as S1 and rapid eye movement. The total number of EEG channels of healthy humans and bruxism patients analyzed in this work were 15 and 18, respectively. The results showed that the individual accuracy of the C4-P4 and C4-A1 channels was 81.70% and 74.11%, respectively. The combined accuracy of both C4-P4 and C4-A1 channels was 81.25%. The specificity of combined result was higher than individual. In addition, the value of theta activity during detection is consistent throughout the period, and the accuracy of S1 stage is better than rapid eye movement stage. We proposed that the theta activity of S1 could be taken for the detection of bruxism. The proposed approach in the detection of the bruxism is negligible in noise as it is in mathematical form and has taken very less time as compared with the traditional systems. The present research work would provide a fast and effective detection system of the sleep bruxism with high accuracy for medical big data applications.

Highlights

Sleep has a crucial role in the life of zoological species such as animals, amphibians, birds, humans, mammals, and reptiles [1]
The C4-P4 and C4-A1 channels extracted from EEG signal for the both healthy humans and bruxism patients (Fig. 1(C) and Fig. 1(D)), respectively
The estimation of the power spectral density of the healthy humans and bruxism patients of the C4-P4 and C4-A1 channels of the EEG signal were performed by the Welch methods, which converts the signal from time domain into the frequency domain

Summary

Introduction

Sleep has a crucial role in the life of zoological species such as animals, amphibians, birds, humans, mammals, and reptiles [1]. Some species complete their sleep by closing their eyes such as human beings and most of the animals. If sleep does not complete properly in humans, it leads to several diseases such as bruxism [4], [5], sleep apnea, insomnia [6]–[10], rapid eye movement behavioral disorder (RBD) [11], nocturnal frontal lobe epilepsy (NFLE) [12], narcolepsy [13], and periodic limb movement disorder (PLMD). It damages several organs including the heart, brain and other organs

Objectives

Results

Discussion

Conclusion