ECG PVC Classification Algorithm based on Fusion SVM and Wavelet Transform

Dong, Huang; Liao, Zheng-quan; Changbin, Li; Li, Dan; Wendong, Huang

doi:10.14257/ijsip.2015.8.1.17

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Considering Table 6, it is evident that the accuracy, sensitivity, and specificity of the suggested classification approach surpass those of Mitra and Samanta [13], Jenny et al [1], Dong et al [20], Rizal et al [19], and Liu et al [29].…”

Section: Resultsmentioning

confidence: 92%

“…The PhysioNet/CINC 2020 and 2021 Challenges [17,18] provide an opportunity to discuss the complexities of ECG classification from several perspectives and the impact of analysing large numbers of leads. Algorithms for ECG classification can be divided into two groups: morphologybased methods [1,[13][14][15][19][20][21][22][23] and deep learning-based methods [16,[24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

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Premature Ventricular Contraction Detection Based on Chebyshev Polynomials and K Nearest Neighbours Classifier

Guendouzi¹,

Attari²

2023

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Premature ventricular contraction (PVC) is among the most prevalent forms of arrhythmia diagnosed in clinical settings. Arrhythmias can be recognised by analysing the ECG signal. However, it takes a lot of time for cardiologists to analyse these long-term ECG signals. The fast and accurate identification of PVCs is crucial in the treatment of cardiac diseases Here; we propose a simple and promising method for detecting PVCs in long-term ECG signals.The method is based on Chebyshev polynomial coefficients and the k-nearest neighbour (KNN) classifier. The proposed approach has been experienced on the MIT-BIH Arrhythmia Database and the results of the experiments indicate high levels of accuracy, sensitivity, and specificity, with a 99.35% accuracy rate, 99.86% sensitivity rate, and 85.11% specificity rate. The results are highly pleasing, taking into account the straightforwardness of the classification system. It is possible that the suggested approach to classification could serve as an effective means of diagnosing arrhythmias.

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Section: Resultsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Premature Ventricular Contraction Detection Based on Chebyshev Polynomials and K Nearest Neighbours Classifier

Guendouzi¹,

Attari²

2023

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“…SVM is a learning method based on the minimization criterion, compared with some traditional learning methods, which obviously and has better generalization ability. In the SVM solution process will eventually translate into Problem solving optimal classification plane can be transformed into a constrained optimization problem, shown as below [10]:…”

Section: A Support Vector Machine Theorymentioning

confidence: 99%

On-line Signature Verification Based on GA-SVM

Dong¹,

Gao²

2015

Int. J. Onl. Eng.

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Abstract-With the development of pen-based mobile device, on-line signature verification is gradually becoming a kind of important biometrics verification. This thesis proposes a method of verification of on-line handwritten signatures using both Support Vector Data Description (SVM) and Genetic Algorithm (GA). A 27-parameter feature set including shape and dynamic features is extracted from the on-line signatures data. The genuine signatures of each subject are treated as target data to train the SVM classifier. As a kernel based one-class classifier, SVM can accurately describe the feature distribution of the genuine signatures and detect the forgeries. To improving the performance of the authentication method, genetic algorithm (GA) is used to optimise classifier parameters and feature subset selection. Signature data form the SVC2013 database is used to carry out verification experiments. The proposed method can achieve an average Equal Error Rate (EER) of 4.93% of the skill forgery database.

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