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Authors: J. H. Oliveira ; V. Ferreira and M. Coimbra

Affiliation: Universidade do Porto, Portugal

ISBN: 978-989-758-069-7

Keyword(s): Deterministic Analysis, Heart Signal Processing, Predictability.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Biomedical Engineering ; Biomedical Signal Processing ; Cardiovascular Signals ; Computer Vision, Visualization and Computer Graphics ; Medical Image Detection, Acquisition, Analysis and Processing

Abstract: The first step in any non linear time series analysis, is to characterize signals in terms of periodicity, stationarity ,linearity and predictability. In this work we aim to find if PCG (phonocardiogram) and ECG (electrocardiogram) time series are generated by a deterministic system and not from a random stochastic process. If PCG and ECG are non-linear deterministic systems and they are not very contaminated with noise, data should be confined to a finite dimensional manifold, which means there are structures hidden under the signal that could be used to increase our knowledge in forecasting future values of the time series. A non-linear process can give rise to very complex dynamic behaviours, even though the underlying process is purely deterministic and probably low-dimensional. To test this hypothesis, we have generated 99 surrogates and then we compared the fitting capability of AR (auto-regressive) models on the original and surrogate data. The results show with a 99% of confid ence level that PCG and ECG were generated by a deterministic process. We compared the fitting capability of an ECG and PCG to AR linear models, using a multi-channel approach. We make an assumption that if a signal is more linearly predictable than another one, it may adjust better to these AR linear models. The results showed that ECG is more linearly predictable (for both channels) than PCG, although a filtering step is needed for the first channel. Finally we show that the false nearest neighbour method is insufficient to identify the correct dimension of the attractor in the reconstructed state space for both PCG and ECG signals. (More)

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Paper citation in several formats:
H. Oliveira, J.; H. Oliveira, J.; Ferreira, V. and Coimbra, M. (2015). Can We Find Deterministic Signatures in ECG and PCG Signals?.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015) ISBN 978-989-758-069-7, pages 184-189. DOI: 10.5220/0005205201840189

@conference{biosignals15,
author={J. H. Oliveira. and J. H. Oliveira. and V. Ferreira. and M. Coimbra.},
title={Can We Find Deterministic Signatures in ECG and PCG Signals?},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)},
year={2015},
pages={184-189},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005205201840189},
isbn={978-989-758-069-7},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2015)
TI - Can We Find Deterministic Signatures in ECG and PCG Signals?
SN - 978-989-758-069-7
AU - H. Oliveira, J.
AU - H. Oliveira, J.
AU - Ferreira, V.
AU - Coimbra, M.
PY - 2015
SP - 184
EP - 189
DO - 10.5220/0005205201840189

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