NEUROPHYSIOLOGIC AND STATISTICAL ANALYSIS OF FAILURES IN AUTOMATIC SLEEP STAGE CLASSIFICATION

Teresa Sousa, Dulce Oliveira, Sirvan Khalighi, Gabriel Pires, Urbano Nunes

Abstract

This paper analyses some of the challenges in automatic multiclass sleep stage classification. Six electroencephalographic (EEG) and two electrooculographic (EOG) channels were used in this study. A set of significant features are selected by a minimum-redundancy maximum-relevance (mRMR) criterion and then classified using support vector machine (SVM). The system is tested on 14 subjects suspected of having sleep apnea. The automatic sleep staging showed a 77.70% (±15.8) sensitivity and 95.49% (±2.68) specificity. From the analysis comparing EEG records with visual and automatic classification, we found that the main cause of failures are the similarities between adjacent phases of sleep, in particular in discriminating N1 and N2. Based on the variation of the values of the features it is possible to implement some thresholds and to apply some heuristic rules to improve the performance.

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Paper Citation


in Harvard Style

Sousa T., Oliveira D., Khalighi S., Pires G. and Nunes U. (2012). NEUROPHYSIOLOGIC AND STATISTICAL ANALYSIS OF FAILURES IN AUTOMATIC SLEEP STAGE CLASSIFICATION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 423-428. DOI: 10.5220/0003792304230428


in Bibtex Style

@conference{biosignals12,
author={Teresa Sousa and Dulce Oliveira and Sirvan Khalighi and Gabriel Pires and Urbano Nunes},
title={NEUROPHYSIOLOGIC AND STATISTICAL ANALYSIS OF FAILURES IN AUTOMATIC SLEEP STAGE CLASSIFICATION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={423-428},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003792304230428},
isbn={978-989-8425-89-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - NEUROPHYSIOLOGIC AND STATISTICAL ANALYSIS OF FAILURES IN AUTOMATIC SLEEP STAGE CLASSIFICATION
SN - 978-989-8425-89-8
AU - Sousa T.
AU - Oliveira D.
AU - Khalighi S.
AU - Pires G.
AU - Nunes U.
PY - 2012
SP - 423
EP - 428
DO - 10.5220/0003792304230428