ArticleFrontiers in neuroscience2021
Sleep Quality Detection Based on EEG Signals Using Transfer Support Vector Machine Algorithm.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- State-Dependent CNN-GRU Reinforcement Framework for Robust EEG-Based Sleep Stage Classification.Biomimetics (Basel, Switzerland) · 2026Article
- Detailed assessment of night shift work aspects and potential mediators of its health effects: the contribution of field studies.Frontiers in public health · 2025Article
- An EEG-based machine learning framework for diagnosing acute sleep deprivation.Frontiers in physiology · 2025Article
- Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview.Micromachines · 2022Review
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1 author.
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Abstract
backgroundIn recent years, with the acceleration of life rhythm and increased pressure, the problem of sleep disorders has become more and more serious. It affects people's quality of life and reduces work efficiency, so the monitoring and evaluation of sleep quality is of great significance. Sleep staging has an important reference value in sleep quality assessment. This article starts with the study of sleep staging to detect and analyze sleep quality. For the purpose of sleep quality detection, this article proposes a sleep quality detection method based on electroencephalography (EEG) signals. MATERIALS AND
methodsThis method first preprocesses the EEG signals and then uses the discrete wavelet transform (DWT) for feature extraction. Finally, the transfer support vector machine (TSVM) algorithm is used to classify the feature data.
resultsThe proposed algorithm was tested using 60 pieces of data from the National Sleep Research Resource Library of the United States, and sleep quality was evaluated using three indicators: sensitivity, specificity, and accuracy. Experimental results show that the classification performance of the TSVM classifier is significantly higher than those of other comparison algorithms. This further validated the effectiveness of the proposed sleep quality detection method.
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