Classification methodology of CVD with localized feature analysis using Phase Space Reconstruction targeting personalized remote health monitoring

Abstract

This paper introduces the classification methodology of Cardiovascular Disease (CVD) with localized feature analysis using Phase Space Reconstruction (PSR) technique targeting personalized health care. The proposed classification methodology uses a few localized features (QRS interval and PR interval) of individual Electrocardiogram (ECG) beats from the Feature Extraction (FE) block and detects the desynchronization in the given intervals after applying the PSR technique. Considering the QRS interval, if any notch is present in the QRS complex, then the corresponding contour will appear and the variation in the box count indicating a notch in the QRS complex. Likewise, the contour and the disparity of box count due to the variation in the PR interval localized wave have been noticed using the proposed PSR technique. ECG database from the Physionet (MIT-BIH and PTBDB) has been used to verify the proposed analysis on localized features using proposed PSR and has enabled us to classify the various abnormalities like fragmented QRS complexes, myocardial infarction, ventricular arrhythmia and atrial fibrillation. The design have been successfully tested for diagnosing various disorders with 98% accuracy on all the specified abnormal databases.

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Cite this paper

@article{Vemishetty2016ClassificationMO, title={Classification methodology of CVD with localized feature analysis using Phase Space Reconstruction targeting personalized remote health monitoring}, author={Naresh Vemishetty and Amit Acharyya and Saptarshi Das and Shivteja Ayyagari and Soumya Jana and Koushik Maharatna and Paolo Emilio Puddu}, journal={2016 Computing in Cardiology Conference (CinC)}, year={2016}, pages={437-440} }