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Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database
Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on t...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702318/ https://www.ncbi.nlm.nih.gov/pubmed/34956556 http://dx.doi.org/10.1155/2021/1819112 |
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author | Yan, Wei Zhang, Zhen |
author_facet | Yan, Wei Zhang, Zhen |
author_sort | Yan, Wei |
collection | PubMed |
description | Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency. |
format | Online Article Text |
id | pubmed-8702318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-87023182021-12-24 Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database Yan, Wei Zhang, Zhen J Healthc Eng Research Article Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency. Hindawi 2021-12-16 /pmc/articles/PMC8702318/ /pubmed/34956556 http://dx.doi.org/10.1155/2021/1819112 Text en Copyright © 2021 Wei Yan and Zhen Zhang. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yan, Wei Zhang, Zhen Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database |
title | Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database |
title_full | Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database |
title_fullStr | Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database |
title_full_unstemmed | Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database |
title_short | Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database |
title_sort | online automatic diagnosis system of cardiac arrhythmias based on mit-bih ecg database |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8702318/ https://www.ncbi.nlm.nih.gov/pubmed/34956556 http://dx.doi.org/10.1155/2021/1819112 |
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