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Authors: Ali Moukadem 1 ; Alain Dieterlen 1 and Christian Brandt 2

Affiliations: 1 University of Haute Alsace, France ; 2 University Hospital of Strasbourg, France

ISBN: 978-989-8425-89-8

Keyword(s): Heart sounds, Singular value decomposition, Time-frequency analysis, Feature extraction, Empirical mode decomposition, s-Transform.

Related Ontology Subjects/Areas/Topics: Acoustic Signal Processing ; Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Cardiovascular Signals ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing, Sensors, Systems Modeling and Control ; Soft Computing ; Time and Frequency Response ; Time-Frequency Analysis

Abstract: Most of the existing methods for the segmentation of heart sounds use the feature of systole and diastole duration to classify the first heart sound (S1) and the second heart sound (S2). These time intervals can become problematic and useless in several clinical real life settings which are particularly represented by severe tachycardia or in tachyarrhythmia. Consequently with the objective of development of a robust generic module for heart sound segmentation we propose to study two methods of extraction based on Singular Value Decomposition (SVD) technique to distinguish S1 from S2. A K-Neirest Neighbor (KNN) classifier is used to estimate the performance of each feature extraction method. The study uses a database with 80 subjects, including 40 cardiac pathologic sounds which contain different systolic murmurs and tachycardia cases. The first and the second proposed method reached 96 % and 95% correct classification rates, respectively.

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Paper citation in several formats:
Moukadem, A.; Dieterlen, A. and Brandt, C. (2012). STUDY OF TWO FEATURE EXTRACTION METHODS TO DISTINGUISH BETWEEN THE FIRST AND THE SECOND HEART SOUNDS.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 346-350. DOI: 10.5220/0003743703460350

@conference{biosignals12,
author={Ali Moukadem. and Alain Dieterlen. and Christian Brandt.},
title={STUDY OF TWO FEATURE EXTRACTION METHODS TO DISTINGUISH BETWEEN THE FIRST AND THE SECOND HEART SOUNDS},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={346-350},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003743703460350},
isbn={978-989-8425-89-8},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - STUDY OF TWO FEATURE EXTRACTION METHODS TO DISTINGUISH BETWEEN THE FIRST AND THE SECOND HEART SOUNDS
SN - 978-989-8425-89-8
AU - Moukadem, A.
AU - Dieterlen, A.
AU - Brandt, C.
PY - 2012
SP - 346
EP - 350
DO - 10.5220/0003743703460350

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