Spectral Data Fusion for Robust ECG-derived Respiration with Experiments in Different Physical Activity Levels

Iman Alikhani, Kai Noponen, Arto Hautala, Rahel Ammann, Tapio Seppänen

Abstract

In this paper, we study instant respiratory frequency extraction using single-channel electrocardiography (ECG) during mobile conditions such as high intensity exercise or household activities. Although there are a variety of ECG-derived respiration (EDR) methods available in the literature, their performance during such activities is not very well-studied. We propose a technique to boost the robustness and reliability of widely used and computationally efficient EDR methods, aiming to qualify them for ambulatory and daily monitoring. We fuse two independent sources of respiratory information available in ECG signal, including respiratory sinus arrhythmia (RSA) and morphological change of ECG time series, to enhance the accuracy and reliability of instant breathing rate estimation during ambulatory measurements. Our experimental results show that the fusion method outperforms individual methods in four different protocols, including household and sport activities.

References

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Paper Citation


in Harvard Style

Alikhani I., Noponen K., Hautala A., Ammann R. and Seppänen T. (2017). Spectral Data Fusion for Robust ECG-derived Respiration with Experiments in Different Physical Activity Levels . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 88-95. DOI: 10.5220/0006144100880095


in Bibtex Style

@conference{healthinf17,
author={Iman Alikhani and Kai Noponen and Arto Hautala and Rahel Ammann and Tapio Seppänen},
title={Spectral Data Fusion for Robust ECG-derived Respiration with Experiments in Different Physical Activity Levels},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)},
year={2017},
pages={88-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006144100880095},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, (BIOSTEC 2017)
TI - Spectral Data Fusion for Robust ECG-derived Respiration with Experiments in Different Physical Activity Levels
SN - 978-989-758-213-4
AU - Alikhani I.
AU - Noponen K.
AU - Hautala A.
AU - Ammann R.
AU - Seppänen T.
PY - 2017
SP - 88
EP - 95
DO - 10.5220/0006144100880095