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Authors: Tiago Marçal 1 ; José Basílio Simões 1 ; José Moutinho dos Santos 2 ; Agostinho Rosa 3 and João Cardoso 1

Affiliations: 1 University of Coimbra, Portugal ; 2 Centro Hospitalar e Universitário de Coimbra, Portugal ; 3 Technical University of Lisbon, Portugal

ISBN: 978-989-8565-36-5

Keyword(s): Snoring, Shannon’s Entropy, Pulse Energy, Sleep, Sleep Disorders, Obstructive Sleep Apnea Syndrome, Acoustic Analysis.

Related Ontology Subjects/Areas/Topics: Acoustic Signal Processing ; Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Fuzzy Systems and Signals ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing ; Wavelet Transform

Abstract: Snoring is a widely occurring problem in our society and it is highly associated with pathologies like Obstructive Sleep Apnea Syndrome (OSAS) being, usually, one of the first symptoms to appear. Economically, OSAS has a great impact since sleep disorders affect the daily performance of people in their professional activities. The extensive study of snoring evidences may be useful to improve the knowledge of associated pathologies, such as OSAS or others, at an early state. In this work, we study full night sound recordings of patients undergoing polysomnography (PSG) procedures. Recordings are offline processed to characterize time series of snoring events through the record length and correlated with the PSG data. The main goal of the proposed algorithms is to understand the behaviour of the full night sound recording and to identify snoring event patterns that may help and refine the diagnostics process. To achieve this goal, the relationship between the energy and the entropy was studied, for each respiratory event, in both snoring and non-snoring cases. Recordings are offline processed to characterize time series of snoring events through the record length and correlated with the PSG data. In the future, the relationship between these two physical variables can be used to predict the clinical evolution between a simple snorer patient and a patient with OSAS. (More)

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Paper citation in several formats:
Marçal, T.; Basílio Simões, J.; Moutinho dos Santos, J.; Rosa, A. and Cardoso, J. (2013). Snoring Analysis on Full Night Recordings based in the Energy and Entropy in PSG Basal Studies.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013) ISBN 978-989-8565-36-5, pages 221-227. DOI: 10.5220/0004245202210227

@conference{biosignals13,
author={Tiago Mar\c{C}al. and José Basílio Simões. and José Moutinho dos Santos. and Agostinho Rosa. and João Cardoso.},
title={Snoring Analysis on Full Night Recordings based in the Energy and Entropy in PSG Basal Studies},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)},
year={2013},
pages={221-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004245202210227},
isbn={978-989-8565-36-5},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2013)
TI - Snoring Analysis on Full Night Recordings based in the Energy and Entropy in PSG Basal Studies
SN - 978-989-8565-36-5
AU - Marçal, T.
AU - Basílio Simões, J.
AU - Moutinho dos Santos, J.
AU - Rosa, A.
AU - Cardoso, J.
PY - 2013
SP - 221
EP - 227
DO - 10.5220/0004245202210227

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