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Authors: Monika Simjanoska 1 ; Martin Gjoreski 2 ; Ana Madevska Bogdanova 1 ; Bojana Koteska 1 ; Matjaž Gams 2 and Jurij Tasič 3

Affiliations: 1 Ss. Cyril and Methodius University, Macedonia, The Former Yugoslav Republic of ; 2 Jozef Stefan Institute and International Postgraduate School, Slovenia ; 3 University of Ljubljana, Slovenia

ISBN: 978-989-758-281-3

Keyword(s): Blood Pressure, ECG-derived, Complexity Analysis, Machine Learning, Stacking, Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Data Mining ; Databases and Information Systems Integration ; Devices ; Enterprise Information Systems ; Health Information Systems ; Human-Computer Interaction ; Pattern Recognition and Machine Learning ; Physiological Computing Systems ; Physiological Modeling ; Sensor Networks ; Signal Processing ; Soft Computing ; Wearable Sensors and Systems

Abstract: The recent advancement on wearable physiological sensors supports the development of real-time diagnosis in preventive medicine that demands various signal processing techniques to enable the extraction of the vital signs (e.g., blood pressure). Blood pressure estimation from physiological sensors data is challenging task that usually is solved by a combination of multiple signals. In this paper we present a novel complexity analysis-based machine-learning perspective on the problem of blood pressure class estimation only from ECG signals. We show that high classification accuracy of 96.68% can be achieved by extracting information via complexity analysis on the ECG signal followed by applying a stack of machine-learning classifiers. In addition, the proposed stacking approach is compared to a traditional machine-learning approaches and feature analysis is performed to determine the influence of the different features on the classification accuracy. The experimental data was gathered by daily monitoring of 20 subjects with two different ECG sensors. (More)

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Paper citation in several formats:
Simjanoska, M.; Gjoreski, M.; Madevska Bogdanova, A.; Koteska, B.; Gams, M. and Tasič, J. (2018). ECG-derived Blood Pressure Classification using Complexity Analysis-based Machine Learning.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF, ISBN 978-989-758-281-3, pages 282-292. DOI: 10.5220/0006538202820292

@conference{healthinf18,
author={Monika Simjanoska. and Martin Gjoreski. and Ana Madevska Bogdanova. and Bojana Koteska. and Matjaž Gams. and Jurij Tasič.},
title={ECG-derived Blood Pressure Classification using Complexity Analysis-based Machine Learning},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF,},
year={2018},
pages={282-292},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006538202820292},
isbn={978-989-758-281-3},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF,
TI - ECG-derived Blood Pressure Classification using Complexity Analysis-based Machine Learning
SN - 978-989-758-281-3
AU - Simjanoska, M.
AU - Gjoreski, M.
AU - Madevska Bogdanova, A.
AU - Koteska, B.
AU - Gams, M.
AU - Tasič, J.
PY - 2018
SP - 282
EP - 292
DO - 10.5220/0006538202820292

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