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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/21/4708 |
| _version_ | 1817994950444318720 |
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| author | Javier Tejedor Constantino A. García David G. Márquez Rafael Raya Abraham Otero |
| author_facet | Javier Tejedor Constantino A. García David G. Márquez Rafael Raya Abraham Otero |
| author_sort | Javier Tejedor |
| collection | DOAJ |
| 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 <i>best</i> 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. |
| first_indexed | 2024-04-14T01:58:47Z |
| format | Article |
| id | doaj.art-43869174fcc740a690ac43681f9cefde |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T01:58:47Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-43869174fcc740a690ac43681f9cefde2022-12-22T02:18:53ZengMDPI AGSensors1424-82202019-10-011921470810.3390/s19214708s19214708Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A ReviewJavier Tejedor0Constantino A. García1David G. Márquez2Rafael Raya3Abraham Otero4Department of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, SpainDepartment of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, SpainDepartment of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, SpainDepartment of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, SpainDepartment of Information Technology, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Campus Montepríncipe, Boadilla del Monte, 28668 Madrid, SpainThis 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 <i>best</i> 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.https://www.mdpi.com/1424-8220/19/21/4708fusionelectrocardiogramphysiological signalsheartbeat detection |
| spellingShingle | Javier Tejedor Constantino A. García David G. Márquez Rafael Raya Abraham Otero Multiple Physiological Signals Fusion Techniques for Improving Heartbeat Detection: A Review Sensors fusion electrocardiogram physiological signals heartbeat detection |
| 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 | fusion electrocardiogram physiological signals heartbeat detection |
| url | https://www.mdpi.com/1424-8220/19/21/4708 |
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