Volume 15, Issue 1 (6-2018)                   JSDP 2018, 15(1): 55-70 | Back to browse issues page

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Rahbaripour M, Mohammadzadeh Asl B. Premature Ventricular Contraction Arrhythmia Detection in ECG Signals via Combined Classifiers. JSDP. 2018; 15 (1) :55-70
URL: http://jsdp.rcisp.ac.ir/article-1-584-en.html
Assistant Professor Tarbiat Modares University
Abstract:   (301 Views)

Cardiovascular diseases are the most dangerous diseases and one of the biggest causes of fatality all over the world. One of the most common cardiac arrhythmias which has been considered by physicians is premature ventricular contraction (PVC) arrhythmia. Detecting this type of arrhythmia due to its abundance of all ages, is particularly important. ECG signal recording is a non-invasive, popular method for an assessment of heart's function. Development of quick, accurate automatic ECG classification methods is essential for the clinical diagnosis of heart disease. This research analyzes the ECG signal to detect PVC arrhythmia. Different techniques are provided in order to detect this type of arrhythmia based on ECG signals. As these techniques use different methods for detection, the reaction of each one will be different to detect this type of arrhythmia. There is no classifier to give the best results for all matters at any time and combining classifiers improve the combined system results in comparison with each of the techniques.
In this study, the MIT-BIH arrhythmia database is used as a data source. Two datasets are used for training; the first contains 2400 samples, as in other studies, and the second contains 600 samples, including normal and PVC beats. Morphological features and features obtained from wavelet transform used in a combined classifier were used afterwards, which is the combination of the most common classifiers namely artificial neural network, SVM and KNN for PVC beat classification. Statistical significance features were selected using the p-value approach and normalized them. The best results were obtained when combining all three classifiers and using normalized statistical significance features. The designed hybrid system succeeded to detect PVC beats with 98.9±0.2% accuracy, 99.0±0.1% sensitivity, and 98.8±0.2% specificity. Also, the efficiency of the proposed method was shown when using limited training samples. The results showed the success of the proposed approach, specifically in comparison with other related research studies.
 

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Type of Study: Research | Subject: Paper
Received: 2016/09/21 | Accepted: 2017/10/28 | Published: 2018/06/13 | ePublished: 2018/06/13

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