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Authors: Shirin Riazy 1 ; Tilo Wendler 1 ; Jürgen Pilz 2 ; M. Glos 3 and T. Penzel 3

Affiliations: 1 Hochschule für Technik und Wirtschaft, Germany ; 2 Alpen-Adria Universität Klagenfurt, Austria ; 3 Charité - Universitätsmedizin Berlin, Germany

ISBN: 978-989-758-212-7

Keyword(s): Automatic Sleep Staging, Two-channel Measurement, Bayesian Statistics, Hidden Markov Model, MAP.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Detection and Identification ; Devices ; Health Information Systems ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Physiological Computing Systems ; Wearable Sensors and Systems

Abstract: In this paper, we shall introduce an algorithm that classifies EEG data into five sleep stages, relying only on two-channel sleep measurements. The sleep of a patient (divided into intervals of 30 seconds) is assumed to be a Markov chain on the five-element state space of sleep stages and our aim is to compute the most probable chain of this hidden Markov model by a maximum a posteriori (MAP) estimation in the Bayesian framework. Both the prior distribution of the chains and the likelihood model have to be trained on manual classifications made by professionals. For this purpose, the data is first preprocessed by a Fourier transform, a log transform and a principal component analysis for dimensionality reduction. Since the number of possible chains is immense (roughly 10^335), a heuristic approach for the computation of the MAP estimator is introduced, that systematically discards unlikely chains. The sleep stage classification is then compared to the classification of a professional, who scores according to the AASM and uses a full polysomnography. The overall structure of the hypnogram can adequately be reconstructed with error rates around 25%. (More)

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Paper citation in several formats:
Riazy, S.; Wendler, T.; Pilz, J.; Glos, M. and Penzel, T. (2017). Heuristic Approximation of the MAP Estimator for Automatic Two-channel Sleep Staging.In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 236-241. DOI: 10.5220/0006242802360241

@conference{biosignals17,
author={Shirin Riazy. and Tilo Wendler. and Jürgen Pilz. and M. Glos. and T. Penzel.},
title={Heuristic Approximation of the MAP Estimator for Automatic Two-channel Sleep Staging},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={236-241},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006242802360241},
isbn={978-989-758-212-7},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - Heuristic Approximation of the MAP Estimator for Automatic Two-channel Sleep Staging
SN - 978-989-758-212-7
AU - Riazy, S.
AU - Wendler, T.
AU - Pilz, J.
AU - Glos, M.
AU - Penzel, T.
PY - 2017
SP - 236
EP - 241
DO - 10.5220/0006242802360241

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