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A comparison between ECG beat classifiers using multiclass SVM and SIMCA with time domain PCA feature reduction

Jannah, N. and Hadjiloucas, S. (2017) A comparison between ECG beat classifiers using multiclass SVM and SIMCA with time domain PCA feature reduction. In: 2017 UKSim-AMSS 19th International Conference on Modelling & Simulation, 5-7 April 2017, Cambridge University (Emmanuel College), pp. 126-131.

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Official URL: http://doi.org/10.1109/UKSim.2017.16

Abstract/Summary

Detection and treatment of arrhythmias has become one of the main goals in cardiac care diagnosis provided by general practitioners. Electrocardiogram (ECG) analysis is one of the most commonly used tools to test and diagnose heart problems. Classification of ECG heartbeats enables the identification of specific arrhythmia or other heart conditions. This paper presents and contrasts the results from two effective ECG arrhythmia classification schemes. The first scheme consists of a principal component analysis (PCA) step for feature reduction at the input vector to the classifier, combined with soft independent modelling of class analogy (SIMCA). The second method uses a multi-class support vector machine (MSVM) classifier to differentiate between four different types of arrhythmia from ECG beats. The four types of beats include Normal (N), Premature Ventricular Contraction (PVC), and Atrial premature contraction (APC) and Right Bundle Branch Block Beat (RBBB). The time domain features were obtained from the St Petersburg INCART 12-lead Arrhythmia Database (incartdb). Between 10 and 30 Principal Components (PCs) were selected for reconstructing individual ECG beats and create the input vector to the classifier. The average classification accuracy of the proposed scheme is 76.83% and 98.33% using MSVM and SIMCA classifier respectively. The SIMCA classification algorithm provided better performance than the MSVM classifiers.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Biological Sciences > Department of Bio-Engineering
ID Code:71288

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