Related Experiment Video
Updated: Apr 7, 2026
Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
Published on: June 5, 2019
Automated diagnosis of coronary artery diseased patients by heart rate variability analysis using linear and
Monappa Gundappa Poddar1, Vinod Kumar, Yash Paul Sharma
1Indian Institute of Technology Roorkee, Electrical Engineering , Roorkee , India and.
Insights
This study evaluated Heart Rate Variability (HRV) analysis for diagnosing Coronary Artery Disease (CAD). The PCA-SVM classifier achieved high accuracy, differentiating normal subjects from CAD patients effectively.
Area of Science:
- Cardiology
- Biomedical Engineering
- Signal Processing
Background:
- Coronary Artery Disease (CAD) poses significant health risks, including myocardial infarction and sudden cardiac death.
- Accurate and early diagnosis of CAD is crucial for effective patient management and improved outcomes.
- Heart Rate Variability (HRV) analysis offers a non-invasive method to assess autonomic nervous system function, potentially indicative of cardiac health.
Purpose of the Study:
- To assess the diagnostic performance of linear and non-linear Heart Rate Variability (HRV) features for classifying Normal (NOR) subjects and Coronary Artery Disease (CAD) patients.
- To develop and evaluate classification software modules utilizing these HRV-derived features.
Main Methods:
- Electrocardiogram (ECG) data were recorded from 60 Normal (NOR) subjects and 64 Coronary Artery Disease (CAD) patients.
- RR interval tachograms were generated, and features were computed using time-domain, frequency-domain, and non-linear HRV analysis methods.
- Principal Component Analysis (PCA) was employed for feature dimension reduction, followed by classification using Probabilistic Neural Network, K-Nearest Neighbour, and Support Vector Machine (SVM) algorithms.
Main Results:
- The Principal Component Analysis combined with Support Vector Machine (PCA-SVM) classifier demonstrated significant diagnostic capability.
- The PCA-SVM classifier achieved an overall accuracy of 91.67%.
- Specific class sensitivities were reported as 86.67% for Normal subjects and 96.67% for Coronary Artery Disease patients.
Conclusions:
- The study successfully differentiated between Normal subjects and Coronary Artery Disease patients using HRV analysis.
- The PCA-SVM classifier, utilizing features from linear and non-linear HRV methods, proved effective for CAD diagnosis.
- These findings suggest the potential of advanced HRV analysis as a valuable tool in the non-invasive diagnosis of Coronary Artery Disease.
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.
More Related Videos
Related Concept Videos
01:30Pulse rhythm
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
01:25Correlation between ECG and Cardiac Cycle
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
01:30Dysrhythmias V: Evaluating Dysrhythmias
01:30Factors Influencing Heart Rate
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
01:30Acute Coronary Syndrome III: Diagnostic Studies