Classification Methodology of CVD with Localized Feature Analysis Using Phase Space Reconstruction Targeting Personalized Remote Health Monitoring

摘要

This paper introduces theudclassification methodology of udCudardiovascularudDiseaseud(udCVDud)udwith loudcalized feature udanalysis using Phase Space Rudeconstruction ud(PSR) udtechniqueudtargeting personalized udhealth careud. The udproposedudclassification methodologyuduses uda fewudlocalized udfeatures (QRS interudval andudPR intervalud) of individual udElectrocardiogram (udECGud)udbeats from the Feature udExtraction (FE) block and detectudsudthe uddesynchronization in udthe given intervals after applying the PSR technique. udConsidering the QRS interval,udif any notch is present in the udQRS complex, then thude corresponding contour will udappear udand udthe variation in the box count indicating a notch in the udQRS complex. Likewise, the contour and the disparity of udbox count due to the variation in the PR interval udlocalized udwave have been noticed using the proposed PSRudtechnique.udECG database from the Phyudsionet (MITud-udBIH and udPTBDB) has udbeen used tudo verify the proposed analysis on udlocalized features using proposed PSR and has enabled us udto classify the various abnormudalities like fragmented QRS udcomplexes, myocardial infarctionud, udventricular arrhythmia udand atrial fibrillation. The design have been successfully udtested for diagnosing various disorders with 98% accuracy udon all the specified abnormal databases.