Sleep Stage Classification Using Random Forest Method

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dc.contributor.authorDweiri, Yazan
dc.contributor.authorAlasasleh,Abeer
dc.contributor.authorShannaq, Yara
dc.contributor.authorJadallah, Shatha
dc.date.accessioned2025-11-25T08:57:40Z
dc.date.available2025-11-25T08:57:40Z
dc.date.issued2022
dc.description.abstractThe aim of this work is to apply Random Forest algorithm to classify REM and NREM sleep stages from a single-channel EEG. The training and performance evaluation of this classifier was performed on open-access data (Physiobank SLEEP-EDF database). A total of 5 features were extracted from 30 s epochs of non-overlapping windows. The proposed classifier has achieved an accuracy of 93.09% and Cohen's kappa of 0.90. The proposed classifier can be implemented on a portable microprocessing unit for in-home neuro-monitoring applications. Keywords: EG Signal,Filtering & Segmentation , Feature Extraction, Classification using Random Forest, Sleep stage output, True Class, Predicted Class, Comparison, Performance Evaluation
dc.identifier.citationDweiri, Y., Jadallah, S., Shannaq, Y., & Alasasleh, A. (2022, April). Sleep Stage Classification Using Random Forest Method. In Proceedings of the 12th International Conference on Biomedical Engineering and Technology (pp. 84-88).
dc.identifier.doihttps://doi.org/10.1145/3535694.3535709
dc.identifier.urihttps://repository.adu.ac.ae/handle/1/7807
dc.language.isoen
dc.publisherACM
dc.titleSleep Stage Classification Using Random Forest Method
dc.typeConference Paper

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