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Correlation between ECG and Cardiac Cycle

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
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Electrocardiogram

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Related Experiment Video

Updated: Jun 8, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Correlation technique and least square support vector machine combine for frequency domain based ECG beat

Saibal Dutta¹, Amitava Chatterjee, Sugata Munshi

¹Heritage Institute of Technology, Electrical Engineering Department, Chowbaga Road, Anandapur, Kolkata, West Bengal 700107, India. saibal_ dutta2001@yahoo.com

Medical Engineering & Physics

|September 14, 2010

Summary

This study introduces an automated tool for classifying electrocardiogram (ECG) beats, improving cardiac arrhythmia detection. The developed system achieves high accuracy, aiding in timely medical intervention for heart conditions.

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Last Updated: Jun 8, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Area of Science:

Biomedical Engineering
Medical Informatics
Cardiology

Background:

Accurate and timely detection of cardiac arrhythmia is crucial for effective medical intervention.
Existing methods for ECG beat classification may lack sufficient accuracy or generalization capabilities.

Purpose of the Study:

To develop an automated medical diagnostic tool for classifying ECG beats.
To classify ECG beats into three categories: normal, premature ventricular contraction (PVC), and other beats.
To demonstrate the generalization capability of the proposed classification scheme.

Main Methods:

Utilized a cross-correlation based approach for feature extraction using cross-spectral density information in the frequency domain.
Developed a Least Square Support Vector Machine (LS-SVM) classifier.
Trained the classifier on a small dataset and tested it on a large dataset from the MIT/BIH arrhythmia database.

Main Results:

Achieved high classification accuracy ranging from 95.51% to 96.12% on 40 files from the MIT/BIH arrhythmia database.
The proposed scheme demonstrated superior performance compared to several competing algorithms.
Successfully classified ECG beats into normal, PVC, and other categories.

Conclusions:

The developed automated tool provides an accurate and efficient method for ECG beat classification.
The cross-correlation and LS-SVM approach shows significant potential for clinical application in arrhythmia detection.
The high accuracy and generalization capability suggest the robustness of the proposed scheme for real-world diagnostic use.