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

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

doi:10.1109/access.2019.2928020

Cited by 68 publications

(43 citation statements)

References 39 publications

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“…The SVM classification techniques deployed for two-state sleep stage classification and achieved a success rate of 92%. Sousa et al [54] focused extraction of entropy features from respective channels of EEG along with temporal features and extracted features are classified with SVM and it has reported an overall accuracy of 86.75%.…”

Section: Sn Computer Sciencementioning

confidence: 99%

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

Satapathy

Loganathan

2021

SN COMPUT. SCI.

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Sleep staging is one of the important methods for the diagnosis of the different types of sleep-related diseases. Manual inspection of sleep scoring is a very time-consuming process, labor-intensive, and requires more human interpretations, which may produce biased results. Therefore, in this paper, we propose an efficient automated sleep staging system to improve sleep staging accuracy. In this work, we extracted both linear and non-linear properties from the input signal. Next to that, a set of optimal features was selected from the extracted feature vector by using a feature reduction technique based on the ReliefF weight algorithm. Finally, the selected features were classified through four machine learning techniques like support vector machine, K-nearest neighbor, decision tree, and random forest. The proposed methodology performed using dualchannel EEG signals from the ISRUC-Sleep dataset under the AASM sleep scoring rules. The performance of the proposed methodology compared with the existing similar methods. In this work, we considered the 10-Fold cross validation strategy; our proposed methods reported the highest classification accuracy of 91.67% with the C4-A1 channel, and 93.8% with the O2-A1 channel using the Random forest classification model. The result of the proposed methodology outperformed the earlier contribution for two-class sleep states classification. The proposed dual-channel sleep staging method can be helpful for the clinicians during the sleep scoring and treatment for the different sleep-related diseases.

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Section: Sn Computer Sciencementioning

confidence: 99%

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

Satapathy

Loganathan

2021

SN COMPUT. SCI.

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“…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%

“…If the person grinds their teeth during awake time, it is called awake bruxism; otherwise, if the person grinds their teeth in their sleep, it is called sleep bruxism [18]. The symptoms of bruxism are grinding or clenching of teeth, flattened, fractured or tipped teeth, pain and sensitivity in teeth, abnormal jaw function, facial pain, earache, and sleep disturbances [19]. The diagnosis of bruxism is through various modalities, including history taking, clinical examination of teeth, intraoral appliances to observe facets, and bite force recording.…”

Section: Introductionmentioning

confidence: 99%

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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“…It is good at classification and regression tasks, and it is interpretable. erefore, the decision tree model has plenty of applications in various fields [24][25][26]. To show the role internet addiction plays when predicting students' online time, we extract students' daily Wi-Fi access time, consuming amount, consuming frequency, average internet access gap, and actual online time.…”

Section: Effect Of Internet Addiction On Online Timementioning

confidence: 99%

Intelligent Behavior Data Analysis for Internet Addiction

Peng

Zhang

2019

Scientific Programming

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Internet addiction refers to excessive internet use that interferes with daily life. Due to its negative impact on college students’ study and life, discovering students’ internet addiction tendencies and making correct guidance for them timely is necessary. However, at present, the research methods used in analyzing students’ internet addiction are mainly questionnaires and statistical analysis, which relies on the domain experts heavily. Fortunately, with the development of the smart campus, students’ behavior data such as consumption and trajectory information in the campus are stored. With this information, we can analyze students’ internet addiction levels quantitatively. In this paper, we provide an approach to estimate college students’ internet addiction levels using their behavior data in the campus. In detail, we consider students’ addiction towards the internet is a hidden variable which affects students’ daily time online together with other behavior. By predicting students’ daily time online, we will find students’ internet addiction levels. Along this line, we develop a linear internet addiction (LIA) model, a neural network internet addiction (NIA) model, and a clustering-based internet addiction (CIA) model to calculate students’ internet addiction levels, respectively. These three models take the regularity of students’ behavior and the similarity among students’ behavior into consideration. Finally, extensive experiments are conducted on a real-world dataset. The experimental results show the effectiveness of our method, and it is also consistent with some psychological findings.

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

Cited by 68 publications

References 39 publications

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

A Study of Human Sleep Stage Classification Based on Dual Channels of EEG Signal Using Machine Learning Techniques

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

Intelligent Behavior Data Analysis for Internet Addiction

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