Premature ventricular contraction detection using swarm-based support vector machine and QRS wave features

Nuryani, Nuryani; Yahya, Iwan; Lestari, Anik

doi:10.1504/ijbet.2014.066224

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…QRS Width is the interval between the beginning and the end of the QRS wave. In this paper by nuryani ,et al [8], QRS width is measured in terms of the interval between two defined points at the QRS wave. To find the RR Interval is to reduce R peak to each other with data Annotation as the cusp.…”

Section: Methodsmentioning

confidence: 99%

Premature ventricular contraction detection using artificial neural network developed in android application

Nugroho

Nuryani

Yahya

et al. 2015

Proceedings of the Joint International Conference on Electric Vehicular Technology and Industrial, Mechanical, Electrical and C

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We have conducted a study of detection system for premature ventricular contraction (PVC) developed in an android mobile phone. The system utilizes artificial neural network (ANN) with electrocardiographic (ECG) features of RR interval and QRS width. RR Interval and QRS width is Interval in ECG waveform. The algorithms of the detection are implemented using JAVA Eclipse Juno. The system is examined using electrocardiography of patients provided by Physionet MIT-BIH. The feature number is varied and the best result is found when both features RR interval and QRS width are applied with the performances of 94.58%, 96.59% and 96.29% in terms of sensitivity, specificity and accuracy Keywords-Premature ventricular contraction, artificial neural network and android device ;

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Section: Methodsmentioning

confidence: 99%

Premature ventricular contraction detection using artificial neural network developed in android application

Nugroho

Nuryani

Yahya

et al. 2015

Proceedings of the Joint International Conference on Electric Vehicular Technology and Industrial, Mechanical, Electrical and C

Self Cite

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“…The width and gradient of QRS wave were given as inputs to SSVM. Nuryani et al (2014) concluded that SSVM with polynomial kernel outperforms SSVM with other kernels like RBF, Sigmoid and linear.…”

Section: Related Workmentioning

confidence: 97%

“…Faezipour et al (2009) reported an accuracy of 99.51% on MIT-BIH arrhythmia database. A Swarm based Support Vector Machine (SSVM) technique is proposed for detecting Premature Ventricular Contraction (PVC) in Nuryani et al (2014). PSO is used to optimise the parameters of SVM.…”

Section: Related Workmentioning

confidence: 99%

ECG beat classification using machine learning techniques

Jambukia

Dabhi

Prajapati

2018

IJBET

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An arrhythmia is an abnormality in the heart rhythm, or heartbeat pattern. ECG beats can be classified into six different arrhythmia beat types (left bundle branch block, right bundle branch block, paced beats, premature ventricular contradiction, atrial premature beats, and normal rhythm). Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. This paper proposes a combination of Particle Swarm Optimisation (PSO) and Feed Forward Neural Network (FFNN) for ECG beat classification. We have used MIT-BIH arrhythmia database for data collection and prepared three different datasets. Features, such as R peak sample number and QRS complex, are extracted using Pan-Tompkins algorithm. The extracted features are used as inputs to three different classifiers: Multi-Layer Perceptron Neural Network (MLPNN), Support Vector Machine (SVM), and PSO-FFNN. Results show high classification accuracy of over 97% with either of these three classifiers. The performance comparison of these classifiers is carried out using three measures: sensitivity, specificity, and accuracy. The results suggest that PSO-FFNN shows slightly better performance than MLPNN and SVM in terms of accuracy on all datasets.

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“…Jenny et al suggested using the independent component analysis (ICA) algorithm to extract features and applying t -test analysis to evaluate these features [ 18 ]. Nuryani et al redefine the width and the gradient of the QRS wave and regarded them as features [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

Wang

Chen

et al. 2021

Biosensors

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Premature ventricular contractions (PVCs), common in the general and patient population, are irregular heartbeats that indicate potential heart diseases. Clinically, long-term electrocardiograms (ECG) collected from the wearable device is a non-invasive and inexpensive tool widely used to diagnose PVCs by physicians. However, analyzing these long-term ECG is time-consuming and labor-intensive for cardiologists. Therefore, this paper proposed a simplistic but powerful approach to detect PVC from long-term ECG. The suggested method utilized deep metric learning to extract features, with compact intra-product variance and separated inter-product differences, from the heartbeat. Subsequently, the k-nearest neighbors (KNN) classifier calculated the distance between samples based on these features to detect PVC. Unlike previous systems used to detect PVC, the proposed process can intelligently and automatically extract features by supervised deep metric learning, which can avoid the bias caused by manual feature engineering. As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. The simulation events show that it is reliable to use deep metric learning and KNN for PVC recognition. More importantly, the overall way does not rely on complicated and cumbersome preprocessing.

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Premature ventricular contraction detection using swarm-based support vector machine and QRS wave features

Cited by 14 publications

References 11 publications

Premature ventricular contraction detection using artificial neural network developed in android application

Premature ventricular contraction detection using artificial neural network developed in android application

ECG beat classification using machine learning techniques

Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN

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