Detection of Premature Ventricular Contraction for real-time applications

Ittatirut, Supat; Lek-uthai, Apiwat; Teeramongkonrasmee, Arporn

doi:10.1109/ecticon.2013.6559531

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…They excluded some records such as those using pacemakers and those containing heart blockage and atrial fibrillation from their experiments [7].…”

Section: Discussionmentioning

confidence: 99%

“…Most of the current studies ( [5] [7] [10] [11] [13]) have used a specific subset of data in the database. In this study, rather than using a specific subset, www.ijacsa.thesai.org almost all PVC beats existing in the database were used.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers attempting to classify PVC arrhythmias have mostly used time-frequency analysis techniques, statistical measurements, and hybrid methods. The most recently published works are those presented in [6][7][8][9][10][11][12][13][14][15]. In [6], the authors applied a dynamic Bayesian network for PVC classification.…”

Section: Introductionmentioning

confidence: 99%

“…In [6], the authors applied a dynamic Bayesian network for PVC classification. In [7], Ittatirut et al attempted to detect PVCs for real-time applications. Their work employed a real-time algorithm for PVC detection based on a low computational method.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], Independent Component Analysis (ICA) was used for feature extraction and k-means and Fuzzy C-Means(FCM) classifiers were employed to recognize the PVC beat. All of these studies [6][7][8][9][10][11][12][13][14][15] used ECG records from the MIT-BIH Arrhythmia Database.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Premature Ventricular Contraction in ECG

Kaya¹,

Pehlivan²

2015

ijacsa

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Abstract-Cardiac arrhythmia is one of the most important indicators of heart disease. Premature ventricular contractions (PVCs) are a common form of cardiac arrhythmia caused by ectopic heartbeats. The detection of PVCs by means of ECG (electrocardiogram) signals is important for the prediction of possible heart failure. This study focuses on the classification of PVC heartbeats from ECG signals and, in particular, on the performance evaluation of time series approaches to the classification of PVC abnormality. Moreover, the performance effects of several dimension reduction approaches were also tested. Experiments were carried out using well-known machine learning methods, including neural networks, k-nearest neighbour, decision trees, and support vector machines. Findings were expressed in terms of accuracy, sensitivity, specificity, and running time for the MIT-BIH Arrhythmia Database. Among the different classification algorithms, the k-NN algorithm achieved the best classification rate. The results demonstrated that the proposed model exhibited higher accuracy rates than those of other works on this topic. According to the experimental results, the proposed approach achieved classification accuracy, sensitivity, and specificity rates of 99.63%, 99.29% and 99.89%, respectively.

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“…They excluded some records such as those using pacemakers and those containing heart blockage and atrial fibrillation from their experiments [7].…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%