Non-Linear Feature Based Sleep Stage Classification

The diagnosis and management of different sleep disorders depend on the categorization of sleepstages. Single channel EEG was used in this study to develop sleep stage classification. The 30-secondepochs of EEG data is decomposed into frequency sub-bands using the Butterworth band-pass filter. Nonlinear features are extracted and different classifiers are used for classification of sleep stages. Variousclassifiers were utilized in the classification of sleep stage. Accuracy ranges for 5-stage classificationranged from 89.36% to 92.88%.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-14
DOI
https://doi.org/10.5281/zenodo.15213910
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
0.00
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article

Non-Linear Feature Based Sleep Stage Classification

Zenodo (CERN European Organization for Nuclear Research)
EEG and Brain-Computer Interfaces
article

Non-Linear Feature Based Sleep Stage Classification

article en

Abstract

The diagnosis and management of different sleep disorders depend on the categorization of sleepstages. Single channel EEG was used in this study to develop sleep stage classification. The 30-secondepochs of EEG data is decomposed into frequency sub-bands using the Butterworth band-pass filter. Nonlinear features are extracted and different classifiers are used for classification of sleep stages. Variousclassifiers were utilized in the classification of sleep stage. Accuracy ranges for 5-stage classificationranged from 89.36% to 92.88%.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 54%
EEG and Brain-Computer Interfaces
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Non-Linear Feature Based Sleep Stage Classification · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS