Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology
ECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias classification solutions with various ECG features have been reported in literature. In this work, a new method combined with a novel morphological feature is proposed for accurate recognition and classification of arrhy...
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doaj-0d53fbba2571418eae6257c62566373f2021-03-30T01:24:14ZengIEEEIEEE Access2169-35362020-01-018471034711710.1109/ACCESS.2020.29792569027930Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG MorphologyHui Yang0https://orcid.org/0000-0003-1211-5609Zhiqiang Wei1https://orcid.org/0000-0002-6965-0162Department of Computer Foundation, Ocean University of China, Qingdao, ChinaCollege of Information Science and Engineering, Ocean University of China, Qingdao, ChinaECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias classification solutions with various ECG features have been reported in literature. In this work, a new method combined with a novel morphological feature is proposed for accurate recognition and classification of arrhythmias. First, the events of the ECG signals are detected. Then, parametric features of ECG morphology, i.e., amplitude, interval and duration, are extracted from selected ECG regions. Next, a novel feature for analyzing QRS complex morphology changes as visual patterns as well as a new clustering-based feature extraction algorithm is proposed. Finally, the feature vectors are applied to three well-known classifiers (neural network, SVM, and KNN) for automatic diagnosis. The proposed method was assessed with all fifteen types of heartbeats as recommended by the Association for Advancement of Medical Instrumentation from the MIT-BIH arrhythmia database and achieved the best overall accuracy of 97.70% based on KNN, using the combined parametric and visual pattern features of ECG Morphology. The accuracies for the six main types - normal (N), left bundled branch blocks (L), right bundled branch blocks (R), premature ventricular contractions (V), atrial premature beats (A) and paced beats (P) are 97.79%, 99.50%, 99.59%, 97.69%, 89.70%, and 99.92%, respectively. Comparisons with peer works prove a marginal progress in automatic heart arrhythmia classification performance.https://ieeexplore.ieee.org/document/9027930/ClassificationECG morphologyfeature extraction |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hui Yang Zhiqiang Wei |
spellingShingle |
Hui Yang Zhiqiang Wei Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology IEEE Access Classification ECG morphology feature extraction |
author_facet |
Hui Yang Zhiqiang Wei |
author_sort |
Hui Yang |
title |
Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology |
title_short |
Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology |
title_full |
Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology |
title_fullStr |
Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology |
title_full_unstemmed |
Arrhythmia Recognition and Classification Using Combined Parametric and Visual Pattern Features of ECG Morphology |
title_sort |
arrhythmia recognition and classification using combined parametric and visual pattern features of ecg morphology |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
ECG is a non-invasive tool used to detect cardiac arrhythmias. Many arrhythmias classification solutions with various ECG features have been reported in literature. In this work, a new method combined with a novel morphological feature is proposed for accurate recognition and classification of arrhythmias. First, the events of the ECG signals are detected. Then, parametric features of ECG morphology, i.e., amplitude, interval and duration, are extracted from selected ECG regions. Next, a novel feature for analyzing QRS complex morphology changes as visual patterns as well as a new clustering-based feature extraction algorithm is proposed. Finally, the feature vectors are applied to three well-known classifiers (neural network, SVM, and KNN) for automatic diagnosis. The proposed method was assessed with all fifteen types of heartbeats as recommended by the Association for Advancement of Medical Instrumentation from the MIT-BIH arrhythmia database and achieved the best overall accuracy of 97.70% based on KNN, using the combined parametric and visual pattern features of ECG Morphology. The accuracies for the six main types - normal (N), left bundled branch blocks (L), right bundled branch blocks (R), premature ventricular contractions (V), atrial premature beats (A) and paced beats (P) are 97.79%, 99.50%, 99.59%, 97.69%, 89.70%, and 99.92%, respectively. Comparisons with peer works prove a marginal progress in automatic heart arrhythmia classification performance. |
topic |
Classification ECG morphology feature extraction |
url |
https://ieeexplore.ieee.org/document/9027930/ |
work_keys_str_mv |
AT huiyang arrhythmiarecognitionandclassificationusingcombinedparametricandvisualpatternfeaturesofecgmorphology AT zhiqiangwei arrhythmiarecognitionandclassificationusingcombinedparametricandvisualpatternfeaturesofecgmorphology |
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1724187097542164480 |