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Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set
Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8158750/ https://www.ncbi.nlm.nih.gov/pubmed/34069374 http://dx.doi.org/10.3390/s21103524 |
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author | Wan, Rongru Huang, Yanqi Wu, Xiaomei |
author_facet | Wan, Rongru Huang, Yanqi Wu, Xiaomei |
author_sort | Wan, Rongru |
collection | PubMed |
description | Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system. |
format | Online Article Text |
id | pubmed-8158750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81587502021-05-28 Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set Wan, Rongru Huang, Yanqi Wu, Xiaomei Sensors (Basel) Article Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system. MDPI 2021-05-19 /pmc/articles/PMC8158750/ /pubmed/34069374 http://dx.doi.org/10.3390/s21103524 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wan, Rongru Huang, Yanqi Wu, Xiaomei Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set |
title | Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set |
title_full | Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set |
title_fullStr | Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set |
title_full_unstemmed | Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set |
title_short | Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set |
title_sort | detection of ventricular fibrillation based on ballistocardiography by constructing an effective feature set |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8158750/ https://www.ncbi.nlm.nih.gov/pubmed/34069374 http://dx.doi.org/10.3390/s21103524 |
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