ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure

Usually, heart failure occurs when heart-related diseases are developed and continue to deteriorate veins and arteries. Heart failure is the final stage of heart disease, and it has become an important medical problem, particularly among the aging population. In medical diagnosis and treatment, the...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Lian Chen, Huiping Yu, Yupeng Huang, Hongyan Jin
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Medicine (General) R5-920 Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/054d313bff8b40fab97afcc9b9e9fbe5
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:054d313bff8b40fab97afcc9b9e9fbe5
record_format	dspace
spelling	oai:doaj.org-article:054d313bff8b40fab97afcc9b9e9fbe52021-11-15T01:19:23ZECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure2040-230910.1155/2021/5802722https://doaj.org/article/054d313bff8b40fab97afcc9b9e9fbe52021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5802722https://doaj.org/toc/2040-2309Usually, heart failure occurs when heart-related diseases are developed and continue to deteriorate veins and arteries. Heart failure is the final stage of heart disease, and it has become an important medical problem, particularly among the aging population. In medical diagnosis and treatment, the examination of heart failure contains various indicators such as electrocardiogram. It is one of the relatively common ways to collect heart failure or attack related information and is also used as a reference indicator for doctors. Electrocardiogram indicates the potential activity of patient’s heart and directly reflects the changes in it. In this paper, a deep learning-based diagnosis system is presented for the early detection of heart failure particularly in elderly patients. For this purpose, we have used two datasets, Physio-Bank and MIMIC-III, which are publicly available, to extract ECG signals and thoroughly examine heart failure. Initially, a heart failure diagnosis model which is based on attention convolutional neural network (CBAM-CNN) is proposed to automatically extract features. Additionally, attention module adaptively learns the characteristics of local features and efficiently extracts the complex features of the ECG signal to perform classification diagnosis. To verify the exceptional performance of the proposed network model, various experiments were carried out in the realistic environment of hospitals. Influence of signal preprocessing on the performance of model is also discussed. These results show that the proposed CBAM-CNN model performance is better for both classifications of ECG signals. Likewise, the CBAM-CNN model is sensitive to noise, and its accuracy is effectively improved as soon as signal is refined.Lian ChenHuiping YuYupeng HuangHongyan JinHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920 Medical technology R855-855.5
spellingShingle	Medicine (General) R5-920 Medical technology R855-855.5 Lian Chen Huiping Yu Yupeng Huang Hongyan Jin ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure
description	Usually, heart failure occurs when heart-related diseases are developed and continue to deteriorate veins and arteries. Heart failure is the final stage of heart disease, and it has become an important medical problem, particularly among the aging population. In medical diagnosis and treatment, the examination of heart failure contains various indicators such as electrocardiogram. It is one of the relatively common ways to collect heart failure or attack related information and is also used as a reference indicator for doctors. Electrocardiogram indicates the potential activity of patient’s heart and directly reflects the changes in it. In this paper, a deep learning-based diagnosis system is presented for the early detection of heart failure particularly in elderly patients. For this purpose, we have used two datasets, Physio-Bank and MIMIC-III, which are publicly available, to extract ECG signals and thoroughly examine heart failure. Initially, a heart failure diagnosis model which is based on attention convolutional neural network (CBAM-CNN) is proposed to automatically extract features. Additionally, attention module adaptively learns the characteristics of local features and efficiently extracts the complex features of the ECG signal to perform classification diagnosis. To verify the exceptional performance of the proposed network model, various experiments were carried out in the realistic environment of hospitals. Influence of signal preprocessing on the performance of model is also discussed. These results show that the proposed CBAM-CNN model performance is better for both classifications of ECG signals. Likewise, the CBAM-CNN model is sensitive to noise, and its accuracy is effectively improved as soon as signal is refined.
format	article
author	Lian Chen Huiping Yu Yupeng Huang Hongyan Jin
author_facet	Lian Chen Huiping Yu Yupeng Huang Hongyan Jin
author_sort	Lian Chen
title	ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure
title_short	ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure
title_full	ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure
title_fullStr	ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure
title_full_unstemmed	ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure
title_sort	ecg signal-enabled automatic diagnosis technology of heart failure
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/054d313bff8b40fab97afcc9b9e9fbe5
work_keys_str_mv	AT lianchen ecgsignalenabledautomaticdiagnosistechnologyofheartfailure AT huipingyu ecgsignalenabledautomaticdiagnosistechnologyofheartfailure AT yupenghuang ecgsignalenabledautomaticdiagnosistechnologyofheartfailure AT hongyanjin ecgsignalenabledautomaticdiagnosistechnologyofheartfailure
_version_	1718428904702607360

ECG Signal-Enabled Automatic Diagnosis Technology of Heart Failure

Ejemplares similares