Work

Non-Linear Feature Based Sleep Stage Classification

Year: 2025

Type: article

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%.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15213909

Author: Anuvitha Chandran V S, Ashalatha Radhakrishnan

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W6912179509

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