Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG
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 p...
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2019-01-01
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| author | Md Belal Bin Heyat Dakun Lai Faez Iqbal Khan Yifei Zhang |
| author_facet | Md Belal Bin Heyat Dakun Lai Faez Iqbal Khan Yifei Zhang |
| author_sort | Md Belal Bin Heyat |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cc8041e43b0e496bb37710a6cd9c7179 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-cc8041e43b0e496bb37710a6cd9c71792025-08-20T03:27:06ZengIEEEIEEE Access2169-35362019-01-01710254210255310.1109/ACCESS.2019.29280208759876Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEGMd Belal Bin Heyat0https://orcid.org/0000-0001-5307-9582Dakun Lai1https://orcid.org/0000-0001-9070-1721Faez Iqbal Khan2https://orcid.org/0000-0001-9088-0723Yifei Zhang3Biomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaBiomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaBiomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaBiomedical Imaging and Electrophysiology Laboratory, School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, ChinaLack 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.https://ieeexplore.ieee.org/document/8759876/Decision treemachine learning classifierneurological disorderscalp EEGsleep bruxism |
| spellingShingle | Md Belal Bin Heyat Dakun Lai Faez Iqbal Khan Yifei Zhang Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG IEEE Access Decision tree machine learning classifier neurological disorder scalp EEG sleep bruxism |
| title | Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG |
| title_full | Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG |
| title_fullStr | Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG |
| title_full_unstemmed | Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG |
| title_short | Sleep Bruxism Detection Using Decision Tree Method by the Combination of C4-P4 and C4-A1 Channels of Scalp EEG |
| title_sort | sleep bruxism detection using decision tree method by the combination of c4 p4 and c4 a1 channels of scalp eeg |
| topic | Decision tree machine learning classifier neurological disorder scalp EEG sleep bruxism |
| url | https://ieeexplore.ieee.org/document/8759876/ |
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