Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network
Cardiac arrhythmia, which means abnormality of heart rhythm, in fact refers to disorder in electrical conduction system of the heart. The aim of this paper is to present a classifier system based on Probabilistic Neural Networks in order to detect and classify abnormal heart rates, where besides its...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca
2013-09-01
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| Series: | Applied Medical Informatics |
| Subjects: | |
| Online Access: | http://ami.info.umfcluj.ro/index.php/AMI/article/view/434/pdf |
| _version_ | 1811285421839089664 |
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| author | Hossein EBRAHIMNEZHAD Shiva KHOSHNOUD |
| author_facet | Hossein EBRAHIMNEZHAD Shiva KHOSHNOUD |
| author_sort | Hossein EBRAHIMNEZHAD |
| collection | DOAJ |
| description | Cardiac arrhythmia, which means abnormality of heart rhythm, in fact refers to disorder in electrical conduction system of the heart. The aim of this paper is to present a classifier system based on Probabilistic Neural Networks in order to detect and classify abnormal heart rates, where besides its simplicity, has high resolution capability. The proposed algorithm has three stages. At first, the electrocardiogram signals impose into preprocessing block. After preprocessing and noise elimination, the exact position of R peak is detected by multi resolution wavelet analysis. In the next step, the extracted linear predictive coefficients (LPC) of QRS complex will enter in to the classification block as an input. A Support Vector Machine classifier is developed in parallel to verify and measure the PNN classifier’s success. The experiments were conducted on the ECG data from the MIT-BIH database to classify four kinds of abnormal waveforms and normal beats such as Normal sinus rhythm, Atrial premature contraction (APC), Right bundle branch block (RBBB) and Left bundle branch block (LBBB). The results show 92.9% accuracy and 93.17% sensitivity |
| first_indexed | 2024-04-13T02:44:53Z |
| format | Article |
| id | doaj.art-482fb5d971e7497bb3eab5e9d6e0c3b2 |
| institution | Directory Open Access Journal |
| issn | 1224-5593 2067-7855 |
| language | English |
| last_indexed | 2024-04-13T02:44:53Z |
| publishDate | 2013-09-01 |
| publisher | Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca |
| record_format | Article |
| series | Applied Medical Informatics |
| spelling | doaj.art-482fb5d971e7497bb3eab5e9d6e0c3b22022-12-22T03:06:05ZengIuliu Hatieganu University of Medicine and Pharmacy, Cluj-NapocaApplied Medical Informatics1224-55932067-78552013-09-013335562Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural NetworkHossein EBRAHIMNEZHADShiva KHOSHNOUDCardiac arrhythmia, which means abnormality of heart rhythm, in fact refers to disorder in electrical conduction system of the heart. The aim of this paper is to present a classifier system based on Probabilistic Neural Networks in order to detect and classify abnormal heart rates, where besides its simplicity, has high resolution capability. The proposed algorithm has three stages. At first, the electrocardiogram signals impose into preprocessing block. After preprocessing and noise elimination, the exact position of R peak is detected by multi resolution wavelet analysis. In the next step, the extracted linear predictive coefficients (LPC) of QRS complex will enter in to the classification block as an input. A Support Vector Machine classifier is developed in parallel to verify and measure the PNN classifier’s success. The experiments were conducted on the ECG data from the MIT-BIH database to classify four kinds of abnormal waveforms and normal beats such as Normal sinus rhythm, Atrial premature contraction (APC), Right bundle branch block (RBBB) and Left bundle branch block (LBBB). The results show 92.9% accuracy and 93.17% sensitivityhttp://ami.info.umfcluj.ro/index.php/AMI/article/view/434/pdfArrhythmiaLinear Predictive CoefficientMulti resolution Wavelet AnalysisProbabilistic Neural Networks |
| spellingShingle | Hossein EBRAHIMNEZHAD Shiva KHOSHNOUD Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network Applied Medical Informatics Arrhythmia Linear Predictive Coefficient Multi resolution Wavelet Analysis Probabilistic Neural Networks |
| title | Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network |
| title_full | Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network |
| title_fullStr | Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network |
| title_full_unstemmed | Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network |
| title_short | Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network |
| title_sort | classification of arrhythmias using linear predictive coefficients and probabilistic neural network |
| topic | Arrhythmia Linear Predictive Coefficient Multi resolution Wavelet Analysis Probabilistic Neural Networks |
| url | http://ami.info.umfcluj.ro/index.php/AMI/article/view/434/pdf |
| work_keys_str_mv | AT hosseinebrahimnezhad classificationofarrhythmiasusinglinearpredictivecoefficientsandprobabilisticneuralnetwork AT shivakhoshnoud classificationofarrhythmiasusinglinearpredictivecoefficientsandprobabilisticneuralnetwork |