Abstract

RODRIGUEZ, C.A. et al. CLASSIFICATION OF PREMATURE VENTRICULAR CONTRACTION BEATS BASED ON UNSUPERVISED LEARNING METHODS. Rev. ing. biomed. [online]. 2014, vol.8, n.15, pp.51-58. ISSN 1909-9762.

Cardiovascular diseases are the principal cause of mortality in the world, so that the development of algorithms that detect cardiac arrhythmias in real time has become an important field of research. The development of these algorithms has led to the improvement of wearable cardiac devices. This paper presents the performance of two algorithms based in unsupervised learning methods for the detection of Premature Ventricular Contraction in the ECG signal. The beats are extracted from MIT-BIH databases, which were preprocessed and segmented by the UPB’s Dynamic Cardiovascular research group. The Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and a proposed hybrid method are implemented for the feature extraction and dimension reduction, from which 8 feature spaces are generated and tested. Kmeans and Self Organizing Maps are developed and compared in terms of accuracy and computational cost. Specificity of 96.22 % and sensitivity of 95.94% with 79.41µs per beat are accomplished. The results show that these methods can be implemented in applications of real time arrhythmia detection because of their low computational cost.

Keywords : Arrhythmia; Premature Ventricular Beat; Discrete Wavelet Transform; Principal Component Analysis; Kmeans; SOM.

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