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A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal
BACKGROUND: Phonocardiogram (PCG) signal provides valuable information for diagnosing heart diseases. However, its applications in quantitative analyses of heart function are limited because the interpretation of this signal is difficult. A key step in quantitative PCG is the identification of the f...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Shiraz University of Medical Sciences
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10258203/ https://www.ncbi.nlm.nih.gov/pubmed/37312888 http://dx.doi.org/10.31661/jbpe.v0i0.2104-1301 |
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author | Movahedi, Mohammad Mehdi Shakerpour, Mohamadreza Mousavi, Shahrokh Nori, Ahmad Mousavian Dehkordi, Seyyed Hesam Parsaei, Hossein |
author_facet | Movahedi, Mohammad Mehdi Shakerpour, Mohamadreza Mousavi, Shahrokh Nori, Ahmad Mousavian Dehkordi, Seyyed Hesam Parsaei, Hossein |
author_sort | Movahedi, Mohammad Mehdi |
collection | PubMed |
description | BACKGROUND: Phonocardiogram (PCG) signal provides valuable information for diagnosing heart diseases. However, its applications in quantitative analyses of heart function are limited because the interpretation of this signal is difficult. A key step in quantitative PCG is the identification of the first and second sounds (S1 and S2) in this signal. OBJECTIVE: This study aims to develop a hardware-software system for synchronized acquisition of two signals electrocardiogram (ECG) and PCG and to segment the recorded PCG signal via the information provided in the acquired ECG signal. MATERIAL AND METHODS: In this analytical study, we developed a hardware-software system for real-time identification of the first and second heart sounds in the PCG signal. A portable device to capture synchronized ECG and PCG signals was developed. Wavelet de-noising technique was used to remove noise from the signal. Finally, by fusing the information provided by the ECG signal (R-peaks and T-end) into a hidden Markov model (HMM), the first and second heart sounds were identified in the PCG signal. RESULTS: ECG and PCG signals from 15 healthy adults were acquired and analyzed using the developed system. The average accuracy of the system in correctly detecting the heart sounds was 95.6% for S1 and 93.4% for S2. CONCLUSION: The presented system is cost-effective, user-friendly, and accurate in identifying S1 and S2 in PCG signals. Therefore, it might be effective in quantitative PCG and diagnosing heart diseases. |
format | Online Article Text |
id | pubmed-10258203 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Shiraz University of Medical Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-102582032023-06-13 A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal Movahedi, Mohammad Mehdi Shakerpour, Mohamadreza Mousavi, Shahrokh Nori, Ahmad Mousavian Dehkordi, Seyyed Hesam Parsaei, Hossein J Biomed Phys Eng Original Article BACKGROUND: Phonocardiogram (PCG) signal provides valuable information for diagnosing heart diseases. However, its applications in quantitative analyses of heart function are limited because the interpretation of this signal is difficult. A key step in quantitative PCG is the identification of the first and second sounds (S1 and S2) in this signal. OBJECTIVE: This study aims to develop a hardware-software system for synchronized acquisition of two signals electrocardiogram (ECG) and PCG and to segment the recorded PCG signal via the information provided in the acquired ECG signal. MATERIAL AND METHODS: In this analytical study, we developed a hardware-software system for real-time identification of the first and second heart sounds in the PCG signal. A portable device to capture synchronized ECG and PCG signals was developed. Wavelet de-noising technique was used to remove noise from the signal. Finally, by fusing the information provided by the ECG signal (R-peaks and T-end) into a hidden Markov model (HMM), the first and second heart sounds were identified in the PCG signal. RESULTS: ECG and PCG signals from 15 healthy adults were acquired and analyzed using the developed system. The average accuracy of the system in correctly detecting the heart sounds was 95.6% for S1 and 93.4% for S2. CONCLUSION: The presented system is cost-effective, user-friendly, and accurate in identifying S1 and S2 in PCG signals. Therefore, it might be effective in quantitative PCG and diagnosing heart diseases. Shiraz University of Medical Sciences 2023-06-01 /pmc/articles/PMC10258203/ /pubmed/37312888 http://dx.doi.org/10.31661/jbpe.v0i0.2104-1301 Text en Copyright: © Journal of Biomedical Physics and Engineering https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 Unported License, ( http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Movahedi, Mohammad Mehdi Shakerpour, Mohamadreza Mousavi, Shahrokh Nori, Ahmad Mousavian Dehkordi, Seyyed Hesam Parsaei, Hossein A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal |
title | A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal |
title_full | A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal |
title_fullStr | A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal |
title_full_unstemmed | A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal |
title_short | A Hardware-Software System for Accurate Segmentation of Phonocardiogram Signal |
title_sort | hardware-software system for accurate segmentation of phonocardiogram signal |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10258203/ https://www.ncbi.nlm.nih.gov/pubmed/37312888 http://dx.doi.org/10.31661/jbpe.v0i0.2104-1301 |
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