In:
Journal of Intelligent & Fuzzy Systems, IOS Press, ( 2023-09-07), p. 1-14
Abstract:
The quality of sleep plays a crucial role in physical well-being, and individuals are becoming increasingly concerned about sleep quality and its associated health issues. Although various sleep monitoring devices exist, there remains a need for a highly accurate sleep state identification algorithm. To address this, we present a paper that utilizes machine learning techniques to identify human sleep states based on electroencephalogram (EEG) signals collected by an EEG instrument. We propose a model that incorporates two nonlinear characteristic parameters, MSE and PSE, extracted from artificially designed EEG signals as input. Additionally, we employ a Support Vector Machine (SVM) classifier algorithm to accurately identify sleep states, eliminating uncertainties associated with manually designed feature parameters. Experimental results demonstrate the superior accuracy of our proposed model for sleep state analysis, offering valuable insights for improving sleep quality and addressing related health concerns.
Type of Medium:
Online Resource
ISSN:
1064-1246
,
1875-8967
Language:
Unknown
Publisher:
IOS Press
Publication Date:
2023
detail.hit.zdb_id:
2070080-5
SSG:
11
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