ArticleBMC oral health2024
Classification of bruxism based on time-frequency and nonlinear features of single channel EEG.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- Artificial Intelligence for Sleep Bruxism Detection: A Technical Pipeline Synthesis.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence-Driven Hemodynamic Monitoring of Simulated Bruxism Using Functional Near-Infrared Spectroscopy: A Preliminary Study.CNS neuroscience & therapeutics · 2025Article
- Preliminary Analysis and Proof-of-Concept Validation of a Neuronally Controlled Visual Assistive Device Integrating Computer Vision with EEG-Based Binary Control.Sensors (Basel, Switzerland) · 2025Article
- A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals.Brain sciences · 2024Article
- Advanced Sensing System for Sleep Bruxism across Multiple Postures via EMG and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Ambulatory devices to detect sleep bruxism: a narrative review.Australian dental journal · 2024Review
- Inflammatory Markers and Sleep Architecture in Sleep Bruxism-A Case-Control Study.Journal of clinical medicine · 2024Article
- A Comprehensive Review on Potential In Silico Screened Herbal Bioactive Compounds and Host Targets in the Cardiovascular Disease Therapy.BioMed research international · 2024Review
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundIn the classification of bruxism patients based on electroencephalogram (EEG), feature extraction is essential. The method of using multi-channel EEG fusing electrocardiogram (ECG) and Electromyography (EMG) signal features has been proved to have good performance in bruxism classification, but the classification performance based on single channel EEG signal is still understudied. We investigate the efficacy of single EEG channel in bruxism classification.
methodsWe have extracted time-domain, frequency-domain, and nonlinear features from single EEG channel to classify bruxism. Five common bipolar EEG recordings from 2 bruxism patients and 4 healthy controls during REM sleep were analyzed. The time domain (mean, standard deviation, root mean squared value), frequency domain (absolute, relative and ratios power spectral density (PSD)), and non-linear features (sample entropy) of different EEG frequency bands were analyzed from five EEG channels of each participant. Fine tree algorithm was trained and tested for classifying sleep bruxism with healthy controls using five-fold cross-validation.
resultsOur results demonstrate that the C4P4 EEG channel was most effective for classification of sleep bruxism that yielded 95.59% sensitivity, 98.44% specificity, 97.84% accuracy, and 94.20% positive predictive value (PPV).
conclusionsOur results illustrate the feasibility of sleep bruxism classification using single EEG channel and provides an experimental foundation for the development of a future portable automatic sleep bruxism detection system.
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