Prognosis of Sleep Bruxism Using Power Spectral Density Approach Applied on EEG Signal of Both EMG1-EMG2 and ECG1-ECG2 Channels

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

59 Citations (Scopus)


Bruxism is a sleep syndrome, in which individual involuntarily grinding and clenching the teeth. If sleep does not complete properly, then it generates many disorders such as bruxism, insomnia, sleep apnea, narcolepsy, rapid eye movement behavioral disorder, and nocturnal frontal lobe epilepsy. The aim of this paper is to draw the results in the form of signal spectrum analysis of the changes in the domain of different stages of sleep. The present research completed in three stages such as the collection of the data, analysis of the electroencephalogram (EEG) signal, and comparative analysis between bruxism patients and normal subjects. Importantly, the channels EMG1-EMG2 and ECG1-ECG2 of the EEG signal were combined for the prognosis of bruxism by using power spectral density, which mainly focused on two sleep stages such as wake (W) and rapid eye movement (REM). The total number of one-minute EEG recordings from bruxism patients and normal subjects analyzed in this work were 149 and 95, respectively. The obtained results show that the average normalized values of the power spectral density of the EMG1-EMG2 and ECG1-ECG2 channels during REM and W sleep stages are several folds higher in case of the bruxism than those in the normal. Moreover, the proposed power spectral density-based method by using the decision tree classifier shows a higher accuracy for the prognosis of sleep bruxism in comparison with previous works. In addition, the proposed approach in the prognosis of the bruxism is noise free and accurate 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 prognosis system of the human bruxism with high accuracy for medical applications.

Original languageEnglish
Article number8743356
Pages (from-to)82553-82562
Number of pages10
JournalIEEE Access
Publication statusPublished - 2019
Externally publishedYes


  • Bruxism
  • decision tree (DT)
  • electrocardiogram (ECG)
  • electroencephalogram (EEG)
  • electromyogram (EMG)
  • power spectral density
  • sleep disorder


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