Automated diagnosis of coronary artery diseased patients by heart rate variability analysis using linear and non-linear methods

Abstract

Coronary artery disease (CAD) is a highly considered dangerous disease which may lead to myocardial infarction and even sudden cardiac death. The objective of this work is to evaluate the diagnostic performance features derived from linear and non-linear methods of Heart Rate Variability (HRV) analysis for classification software modules with Normal (NOR) subjects and CAD patients. The proposed methodology follows the recording of electrocardiogram from 60 NOR subjects and 64 CAD patients, RR interval tachogram generation, computing the features from time domain, frequency domain, non-linear methods and its analysis, feature dimension reduction by Principal Component Analysis (PCA) and classification by probabilistic neural network, K nearest neighbour and Support Vector Machine (SVM) classifiers. The results of the study indicate a clear difference in NOR subjects and CAD affected patients by using PCA-SVM classifier with an accuracy of 91.67%, sensitivity of 86.67% and 96.67% for NOR and CAD classes, respectively.

Detection of Congestive Heart Failure Based on Spectral Features and Extreme Learning Machine

“…Hence proposed method is suitable for classification of cardiac diseases. [44]. Thus, a low p-value shows there is high probability that the individual classes of HRV data sets are detachable [50][51].…”

“…In this condition, a feature dimension transformation technique will be extremely valuable [40]. Different techniques have been developed to reduce the features data size for classification [41][42][43][44]. In this paper, we have applied KPCA for reducing the features dimension.The Kernel principal component analysis (KPCA) is a dimension reduction technique which is also based on nonlinear kernel function [45], but its discriminating capability is higher as compared to GDA for NSR-CHF group subject.…”

HRV Analysis and Ventricular Arrhythmia Classification using various Classifiers

“…For HRV analysis and to classify arrhythmias, the methodology adopted during this research work started from extracting the R-R intervals from the online available MIT-BIH Arrhythmia Database [8] which consists total 48 half an hour recordings. These extracted R-R intervals were used to prepare HRV signals.…”

Prognostic value of plasma von Willebrand factor levels in major adverse cardiovascular events: a systematic review and meta-analysis

“…Inclusion criteria were as follows: (1) cohort studies enrolling patients with CAD (myocardial Infarction, acute coronary syndrome and stable CAD); (2) data on plasma vWF was reported; (3) MACEs or mortality following CAD were recorded; (4) studies written in English or Chinese. Exclusion criteria are as follows: (1) patients without CAD; (2) there is no definitive value of plasma vWF.…”

“…Coronary artery disease (CAD) is characterized by the occlusion or stenosis of coronary artery mostly caused by atherosclerosis, and is one of the leading causes of mortality in humans [1,2]. Patients with CAD are vulnerable in development of major cardiovascular events (MACEs) including nonfatal acute myocardial infarction, unstable angina, stroke, transient ischemic attack, peripheral arterial occlusive disorder, and death [3].…”