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Authors: Filip Plesinger 1 ; Juraj Jurco 1 ; Josef Halamek 1 ; Pavel Leinveber 2 ; T. Reichlova 2 and Pavel Jurak 1

Affiliations: 1 Institute of Scientific Instruments of the Czech Academy of Sciences, Czech Republic ; 2 International Clinical Research Center at St. Anne’s University Hospital, Czech Republic

ISBN: 978-989-758-160-1

Keyword(s): ECG, SAECG, QRS, Ultra-High-Frequency, Clustering, Multi-thread, Ventricle Dyssynchrony.

Related Ontology Subjects/Areas/Topics: Cardiovascular Technologies ; Electrocardiology and Biological Signal Processing ; Health Engineering and Technology Applications ; Risk Stratification Markers (Heart Rate Variability, Heart Rate Turbulence, Etc)

Abstract: Ultra-high-frequency ECG (UHF-ECG) in a range of 500–1,000 Hz has been tested as a new information source for analysis of left-ventricle dyssynchrony and other myocardial abnormalities. The power of UHF signals is extremely low, for which reason an averaging technique is used to improve signal-to-noise ratio. Since ventricle dyssynchrony is different for various QRS complex types, the detected QRS complexes must be clustered into morphology groups prior to averaging. Here, we present a fully-automated method for clustering. The first goal of the method is to separate previously detected QRS complexes into different morphology groups. The second goal is to precisely fit the QRS annotation marks to the exact same position against the QRS shape. The method is based on the Pearson correlation and is optimized for parallel processing. In our application with UHF-ECG data the number of detected groups was 3.24 ± 3.41 (mean and standard deviation over 1,030 records). The method can be used i n other areas also where the clustering of repetitive signal formations is needed. For validation purposes, the method was tested on the MIT-BIH Arrhythmia and INCART databases from Physionet with results of purity of 98.24% and 99.50%. (More)

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Paper citation in several formats:
Plesinger, F.; Jurco, J.; Halamek, J.; Leinveber, P.; Reichlova, T. and Jurak, P. (2015). Multichannel QRS Morphology Clustering - Data Preprocessing for Ultra-High-Frequency ECG Analysis.In Proceedings of the 3rd International Congress on Cardiovascular Technologies - Volume 1: CARDIOTECHNIX, ISBN 978-989-758-160-1, pages 11-19. DOI: 10.5220/0005604200110019

@conference{cardiotechnix15,
author={Filip Plesinger. and Juraj Jurco. and Josef Halamek. and Pavel Leinveber. and T. Reichlova. and Pavel Jurak.},
title={Multichannel QRS Morphology Clustering - Data Preprocessing for Ultra-High-Frequency ECG Analysis},
booktitle={Proceedings of the 3rd International Congress on Cardiovascular Technologies - Volume 1: CARDIOTECHNIX,},
year={2015},
pages={11-19},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005604200110019},
isbn={978-989-758-160-1},
}

TY - CONF

JO - Proceedings of the 3rd International Congress on Cardiovascular Technologies - Volume 1: CARDIOTECHNIX,
TI - Multichannel QRS Morphology Clustering - Data Preprocessing for Ultra-High-Frequency ECG Analysis
SN - 978-989-758-160-1
AU - Plesinger, F.
AU - Jurco, J.
AU - Halamek, J.
AU - Leinveber, P.
AU - Reichlova, T.
AU - Jurak, P.
PY - 2015
SP - 11
EP - 19
DO - 10.5220/0005604200110019

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