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

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

doi:10.1109/access.2019.2924181

Cited by 58 publications

(30 citation statements)

References 38 publications

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“…Previously, we designed the diagnostic of bruxism sleep syndrome and sleep stages using a decision tree classifier with 81.25% accuracy [19]. Lai et al [40] showed that the ECG channel and EMG channel are used for the detection of bruxism syndrome using power spectral density (PSD) techniques. They used the 488-min data for the two channels and classification of the subjects and stages using the machine learning method.…”

Section: Classification Results Using Proposed Novel Hml Classifiermentioning

confidence: 99%

“…The physiological sleep channels, including ECG1-ECG2, EMG1-EMG2, C4-P4, and C4-A1 of the bruxism and healthy subjects, are shown in Figures 3 and 4 [19,40,48]. The 10/20 standard sleep recording system records these channels.…”

Section: Analysis Of the Physiological Signalsmentioning

confidence: 99%

“…The 10/20 standard sleep recording system recorded these data. It is used in the research work of sleep disorders such as circadian rhythm sleep disorders, insomnia [33][34][35][36][37], bruxism [38][39][40], sleep apnea, restless leg syndrome, narcolepsy [41], and nocturnal frontal lobe epilepsy [42]. We preprocessed the signals using a low-pass finite impulse response (FIR) filter to remove the noise of the signal.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

et al. 2020

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Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.

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Section: Classification Results Using Proposed Novel Hml Classifiermentioning

confidence: 99%

Section: Analysis Of the Physiological Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

et al. 2020

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“…Data is typically acquired directly from the human component of the system via the EEG headset, which is analysed with various possible techniques contingent on the psychological information required. Spectral analysis is one of the standard methods used to quantify EEG data, where the Power Spectral Density (PSD) component reflects the distribution of signal power over frequency [28], which is employed for a variety of applications in medicine [29], [30]. In many of these measurement-based systems the subject does not actively, explicitly, or voluntarily modulate EEG activity, instead focussing on the task [31]; this popular configuration is known as pBCI.…”

Section: Related Work a Eeg In Human-machine Systemsmentioning

confidence: 99%

Investigating the P300 Response as a Marker of Working Memory in Virtual Training Environments

Simpson

Rafferty

2021

IEEE Trans. Human-Mach. Syst.

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Conventional performance metrics fail to offer highresolution evaluation of learning and memory during training tasks; the P300 component of the event-related potential (ERP) is a promising tool for enhancing the assessment of training quality in virtual environments, but this technique is yet to be investigated. A driver training simulator and scenario were developed to explore the capability of the P300 for this purpose. A user study was conducted with 32 participants divided into 2 groups objectively determined by driving performance scores, thus enabling observations of the P300 response to be equated to varying levels of learning and memory. Participant electroencephalogram (EEG) data was recorded during the procedure, which was post-processed to filter and extract ERPs to capture neural responses to specific events in the virtual training scenario. These were combined to produce a result for each participant, which was then grand averaged to create an overall ERP for each group. Across the eight electrode sites statistically significant differences were found between the grand average waveforms of the two groups, with high memory retention producing significantly greater peak to peak amplitude (U = 9.00, p = .045), peak latency (U = 0.00, p < .001), and positive area (U = 13.00, p = .05) of the waveform than low memory retention. The evidenced relationship between the P300 response and working memory in this context suggests it has the potential for monitoring learning and memory in stimulus-driven virtual training systems.

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“…Lai et al used the physionet database for the prognosis of bruxism. Their proposed scheme gives high detection accuracy for sleep stages S1 and REM [31]. In this work, a total number of 224 EEG recordings from eight subjects of two sleep stages such as S1 and REM were collected.…”

Section: A Datasetmentioning

confidence: 99%

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

et al. 2019

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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. INDEX TERMS Decision tree, machine learning classifier, neurological disorder, scalp EEG, sleep bruxism.

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

Cited by 58 publications

References 38 publications

A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

Investigating the P300 Response as a Marker of Working Memory in Virtual Training Environments

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

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