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Electrocardiogram

Electrocardiogram

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.
Three major waveforms are present in a typical ECG recording: the P wave, the QRS complex, and...

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.
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...

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Related Experiment Video

Updated: Nov 25, 2025

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Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent

Gyu Ho Choi¹, Hoon Ko¹, Witold Pedrycz²

¹IT Research Institute, Chosun University, Gwangju 61452, Korea.

Sensors (Basel, Switzerland)

|December 16, 2020

Summary

This summary is machine-generated.

This study introduces a novel time and frequency fusion normalization method to improve electrocardiogram (ECG) biometrics. The technique enhances user recognition accuracy by matching pre- and post-exercise ECG signals effectively.

Keywords:

P wave T wave biometrics linear interpolation normalization post-exercise ECG user identification

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Area of Science:

Biometrics
Cardiovascular Signal Processing

Background:

Electrocardiogram (ECG) biometrics face challenges due to environmental variations.
Post-exercise ECG signals often differ morphologically from pre-exercise signals, impacting recognition accuracy.

Purpose of the Study:

To develop a robust method for matching pre- and post-exercise ECG cycles.
To enhance user recognition performance in ECG-based biometrics.

Main Methods:

Proposed a time and frequency fusion normalization method for ECG signal matching.
Utilized linear interpolation and optimized frequency filtering for normalization.
Focused on preserving morphological features like P wave, QRS complexes, and T wave.

Main Results:

Achieved a 25.6% average improvement in similarity between pre- and post-exercise ECG states.
Enhanced maximum user recognition performance from 96.4% to 98% for 30 ECG cycles.

Conclusions:

The proposed fusion normalization method effectively addresses ECG signal variations.
This technique significantly improves the reliability and accuracy of ECG biometrics.