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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 the T...

ECG Interpretation of Rhythms

ECG Interpretation of Rhythms

An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage. When...

Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...

Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
An ECG utilizes electrodes on the skin to...

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: May 27, 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

Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients.

Yakup Kutlu¹, Damla Kuntalp

¹Department of Computer Engineering, Mustafa Kemal University, Hatay, Turkey.

Computer Methods and Programs in Biomedicine

|November 8, 2011

Summary

This study introduces higher-order statistics (HOS) of wavelet packet decomposition (WPD) coefficients for automatic heartbeat recognition. The method accurately classifies arrhythmic ECG beats using these extracted features.

Related Experiment Videos

Last Updated: May 27, 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
Signal Processing
Machine Learning

Background:

Accurate automatic recognition of cardiac arrhythmias from electrocardiogram (ECG) signals is crucial for clinical diagnosis.
Traditional feature extraction methods may not fully capture the complex characteristics of different heartbeat types.

Purpose of the Study:

To develop and evaluate a novel feature extraction technique for automatic heartbeat classification using higher-order statistics (HOS) of wavelet packet decomposition (WPD) coefficients.
To assess the discriminative power of HOS features for classifying various arrhythmic ECG beats.

Main Methods:

Wavelet packet decomposition (WPD) was applied to ECG signals to obtain wavelet packet coefficients (WPC).
Higher-order statistics (HOS) were computed from the WPD coefficients to generate a feature set.
A k-Nearest Neighbors (k-NN) classifier was employed using the extracted HOS features for heartbeat classification.
The MIT-BIH arrhythmia database was utilized, with heartbeats categorized into five main classes.

Main Results:

The proposed system achieved high classification accuracy for different arrhythmic ECG beats.
Average sensitivity reached 90%, average selectivity was 92%, and average specificity was 98%.
HOS of WPC were demonstrated to be highly discriminative features for ECG beat classification.

Conclusions:

Higher-order statistics of wavelet packet decomposition coefficients provide effective and discriminative features for automatic heartbeat recognition.
The proposed HOS-based feature extraction method combined with k-NN classification shows significant potential for accurate arrhythmia detection.