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Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review
This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), el...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864881/ https://www.ncbi.nlm.nih.gov/pubmed/31671921 http://dx.doi.org/10.3390/s19214708 |
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author | Tejedor, Javier García, Constantino A. Márquez, David G. Raya, Rafael Otero, Abraham |
author_facet | Tejedor, Javier García, Constantino A. Márquez, David G. Raya, Rafael Otero, Abraham |
author_sort | Tejedor, Javier |
collection | PubMed |
description | This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out. |
format | Online Article Text |
id | pubmed-6864881 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68648812019-12-06 Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review Tejedor, Javier García, Constantino A. Márquez, David G. Raya, Rafael Otero, Abraham Sensors (Basel) Review This paper presents a review of the techniques found in the literature that aim to achieve a robust heartbeat detection from fusing multi-modal physiological signals (e.g., electrocardiogram (ECG), blood pressure (BP), artificial blood pressure (ABP), stroke volume (SV), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), among others). Techniques typically employ ECG, BP, and ABP, of which usage has been shown to obtain the best performance under challenging conditions. SV, PPG, EMG, EEG, and EOG signals can help increase performance when included within the fusion. Filtering, signal normalization, and resampling are common preprocessing steps. Delay correction between the heartbeats obtained over some of the physiological signals must also be considered, and signal-quality assessment to retain the best signal/s must be considered as well. Fusion is usually accomplished by exploiting regularities in the RR intervals; by selecting the most promising signal for the detection at every moment; by a voting process; or by performing simultaneous detection and fusion using Bayesian techniques, hidden Markov models, or neural networks. Based on the results of the review, guidelines to facilitate future comparison of the performance of the different proposals are given and promising future lines of research are pointed out. MDPI 2019-10-29 /pmc/articles/PMC6864881/ /pubmed/31671921 http://dx.doi.org/10.3390/s19214708 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Tejedor, Javier García, Constantino A. Márquez, David G. Raya, Rafael Otero, Abraham Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review |
title | Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review |
title_full | Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review |
title_fullStr | Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review |
title_full_unstemmed | Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review |
title_short | Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review |
title_sort | multiple physiological signals fusion techniques for improving heartbeat detection: a review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6864881/ https://www.ncbi.nlm.nih.gov/pubmed/31671921 http://dx.doi.org/10.3390/s19214708 |
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