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Automatic sleep scoring using statistical features in the EMD domain and ensemble methods

Paper ID Volume ID Publish Year Pages File Format Full-Text
5126 341 2016 8 PDF Available
Title
Automatic sleep scoring using statistical features in the EMD domain and ensemble methods
Abstract

An automatic sleep scoring method based on single channel electroencephalogram (EEG) is essential not only for alleviating the burden of the clinicians of analyzing a high volume of data but also for making a low-power wearable sleep monitoring system feasible. However, most of the existing works are either multichannel or multiple physiological signal based or yield poor algorithmic performance. In this study, we propound a data-driven and robust automatic sleep staging scheme that uses single channel EEG signal. Decomposing the EEG signal segments using Empirical Mode Decomposition (EMD), we extract various statistical moment based features. The effectiveness of statistical features in the EMD domain is inspected. Statistical analysis is performed for feature selection. We then employ Adaptive Boosting and decision trees to perform classification. The performance of our feature extraction scheme is studied for various choices of classification models. Experimental outcomes manifest that the performance of the proposed sleep staging algorithm is better than that of the state-of-the-art ones. Furthermore, the proposed method's non-REM 1 stage detection accuracy is better than most of the existing works.

Keywords
EEG; AdaBoost; Ensemble learning; Sleep scoring; EMD
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biocybernetics and Biomedical Engineering - Volume 36, Issue 1, 2016, Pages 248–255
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us