Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Wan, Rongru, Huang, Yanqi, Wu, Xiaomei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1783699931116077056
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
work_keys_str_mv AT wanrongru detectionofventricularfibrillationbasedonballistocardiographybyconstructinganeffectivefeatureset
AT huangyanqi detectionofventricularfibrillationbasedonballistocardiographybyconstructinganeffectivefeatureset
AT wuxiaomei detectionofventricularfibrillationbasedonballistocardiographybyconstructinganeffectivefeatureset