Classification of Premature Ventricular Contraction in ECG

Kaya, Yasin; Pehlivan, Hüseyin

doi:10.14569/ijacsa.2015.060706

Cited by 35 publications

(29 citation statements)

References 25 publications

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“…Median signals are selected from three consecutive signal samples to eliminate fluctuations that occur in the signal. This filtering process is expressed in Equation (2) [5].…”

Section: Pre-processingmentioning

confidence: 99%

“…The extraction method used is high order statistics. Meanwhile, in other studies, several classifiers such as SVM, NN, DT with KNN and PCA and ICA were used as feature reduction methods [5]. Both studies resulted in an accuracy of up to 99%.…”

Section: Classification and Validationmentioning

confidence: 99%

“…Kaya and Pehlivan used PCA, ICA, and SOM for data reduction on PVC ECG signal. Neural network (NN), support vector machine (SVM), K-nearest neighbor (KNN), and decision tree (DT) were used as classifiers [5]. Previous researches chose to use feature extraction technique while some only used signal dimensions reduction method.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy

Rizal¹,

Riandini²,

Tresnawati³

2018

IJET

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One of the abnormalities in the heart that can be assessed from an ECG signal is premature ventricle contraction (PVC). PVC is a form of arrhythmia in the form of irregularity in beat ECG signals. In this study, a multilevel wavelet entropy method was developed to distinguish PVC and normal ECG signals automatically. Data was taken from the MIT-BIH arrhythmia database with the process carried out is normalization, median filtering, beat-parsing, MWE calculation and classification using SVM. The results of the experiment showed that MWE level 5 with DB2 as mother wavelet and Quadratic SVM as classifier resulted in the highest accuracy of 94.9%. MWE level 5 means only five features needed for classification. The number of features is very little compared to previous research with a quite high accuracy.

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“…Median signals are selected from three consecutive signal samples to eliminate fluctuations that occur in the signal. This filtering process is expressed in Equation (2) [5].…”

Section: Pre-processingmentioning

confidence: 99%

Section: Classification and Validationmentioning

confidence: 99%

See 1 more Smart Citation

Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy

Rizal¹,

Riandini²,

Tresnawati³

2018

IJET

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“…Premature depolarization of the myocardium in the ventricular region causes PVC and it is a common arrhythmia usually found in adults. It is estimated to have a prevalence of between 1% and 4% of the general population and usually associated with structural heart conditions and increases the risk of sudden death [2].…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al proposed a deep learning method for the recognition of PVC in children [3]. Kaya and Pehlivan compared various methods to classify PVC and investigated the model that gives the best result [2], [4]. Zhou et al used deep neural network (NN) and rule inference to detect PVC [5].…”

Section: Introductionmentioning

confidence: 99%

Classification of PVC Beat in ECG Using Basic Temporal Features

Kaya¹

2018

Balkan Journal of Electrical and Computer Engineering

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Premature ventricular contraction (PVC) is one of the most important arrhythmias among the various hearth abnormalities. Premature depolarization of the myocardium in the ventricular region causes PVC and it is usually associated with structural heart conditions. Arrhythmias can be detected by examining the ECG signal and this review requires large-size data to be examined by physicians. The time spent by the physician in examining the signal can be reduced using CAD systems. In this study, we propose a high performance PVC detection system using the feature extraction and classification scheme bringing low computational burden. The test set consisting of 81844 beats from the MIT-BIH arrhythmia database was used for the experimental results. We compared the performances of the various classifiers using proposed feature set in the experiments and obtained classification accuracy of 98.71% using NN classifier.

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Human machine interfacing technique for diagnosis of ventricular arrhythmia using supervisory machine learning algorithms

Karnan

Sivakumaran

Manivel³

2018

Concurrency and Computation

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Summary The state of art to integrate bio‐signals with computer based diagnosis is taking dominance. The man‐machine interface is useful for early and immediate clinical interpretation. The electrocardiogram (ECG) signal plays a vital role in revealing the possible data towards categorizing normal and abnormal cardiac functioning. The fatal conditions exhibited by ventricular arrhythmias (VA) pose a remarkable change in the feature set of the ECG signals. In this work, a novel approach to segregate the superior feature toward the ventricular arrhythmias are extracted using feature ranking score algorithm (FRSA). The FRSA collects feature vectors in three different domains and ranks it to find out the more prevalent feature for diagnosis of VA. The Support Vector Machine (SVM) classifier is administered by supervisory machine learning optimization algorithm Mean Grey Wolf Optimization (MGWO). The performance estimates of SVM‐MGWO is compared for classification of VA signals with other optimization also like SVM‐Particle Swarm Optimization (SVM‐PSO) and SVM‐Grey Wolf Optimization (SVM‐GWO). The non‐parametric and parametric analysis evidently shows the improved performance of feature parameter estimates for classification. The accuracy of classification for SVM‐MGWO attains 100% for finding test data with VA at a minimal convergence iteration while comparing it with the other mentioned supervisory algorithms. The standard deviation during parametric analysis is negligible, which reveals the fact that reductant feature extracted and utilized for testing of ECG data is minimal. The performance estimates attained by the proposed algorithm shows the selection of optimal feature for the findings of VA through ECG. The man‐machine interface aides in the early diagnosis of ventricular arrhythmias using non‐invasive diagnosing tool, the ECG.

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Classification of Premature Ventricular Contraction in ECG

Cited by 35 publications

References 25 publications

Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy

Premature Ventricular Contraction Classification based on ECG Signal using Multilevel Wavelet entropy

Classification of PVC Beat in ECG Using Basic Temporal Features

Human machine interfacing technique for diagnosis of ventricular arrhythmia using supervisory machine learning algorithms

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