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Authors: Marcel Młyńczak 1 and Gerard Cybulski 2

Affiliations: 1 Warsaw University of Technology, Poland ; 2 Warsaw University of Technology and Polish Academy of Sciences, Poland

ISBN: 978-989-758-212-7

Keyword(s): Ambulatory Monitoring, Impedance Pneumography, Calibration, Neural Networks.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Monitoring and Telemetry ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Impedance pneumography (IP) is mainly used as a noninvasive method to measure respiratory rate, tidal volume or minute ventilation. It could also register flow-related signals, after differentiation, from spirometrybased forced vital capacity maneuvers or ambulatory-based signals reflecting flow values during natural activity. The aim of this paper is to assess the possibility of improving the accuracy of flow parameters calculated by IP, by using nonlinear neural network correction (as opposed to simple linear calibration), and to evaluate the impact of various calibration procedures and neural network configurations. Ten students carried out fixed static breathing sequences, for both calibration and testing. A reference pneumotachometer and the Pneumonitor 2 were used. The validation of calculating peak and mean flow value during each inspiration and expiration was considered. A mean accuracy of 80% was achieved for a separate neural network with two hidden layers with 10 neurons in each layer, trained individually for each subject and body position, using the data from the longest, fixed calibration procedure. Simple linear modeling achieved only 72.5%. (More)

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Paper citation in several formats:
Młyńczak M. and Cybulski G. (2017). Flow Parameters Derived from Impedance Pneumography after Nonlinear Calibration based on Neural Networks.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 70-77. DOI: 10.5220/0006146800700077

@conference{biosignals17,
author={Marcel Młyńczak and Gerard Cybulski},
title={Flow Parameters Derived from Impedance Pneumography after Nonlinear Calibration based on Neural Networks},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},
year={2017},
pages={70-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006146800700077},
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 - Flow Parameters Derived from Impedance Pneumography after Nonlinear Calibration based on Neural Networks
SN - 978-989-758-212-7
AU - Młyńczak M.
AU - Cybulski G.
PY - 2017
SP - 70
EP - 77
DO - 10.5220/0006146800700077

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