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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...

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...

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Related Experiment Video

Updated: Aug 15, 2025

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

A dynamic learning-based ECG feature extraction method for myocardial infarction detection.

Qinghua Sun^1,2, Zhanfei Xu¹, Chunmiao Liang¹

¹School of Control Science and Engineering, Shandong University, Jinan 250061, People's Republic of China.

Physiological Measurement

|January 3, 2023

Summary

This summary is machine-generated.

This study introduces a dynamic learning algorithm to detect myocardial infarction (MI) using electrocardiogram (ECG) signals. The method effectively identifies key ECG features, improving MI diagnosis accuracy.

Keywords:

dynamic feature dynamic learning electrocardiogram hybrid feature selection myocardial infarction

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Last Updated: Aug 15, 2025

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

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Published on: December 28, 2012

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Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

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Published on: February 12, 2011

Area of Science:

Cardiology
Biomedical Engineering
Machine Learning

Background:

Myocardial infarction (MI) is a leading global cause of mortality from cardiovascular diseases.
Electrocardiogram (ECG) is a primary diagnostic tool for MI, but subtle pathological changes are challenging to interpret.
Accurate MI detection requires identifying prominent features from in-depth ECG signal analysis.

Purpose of the Study:

To develop and validate a dynamic learning algorithm for enhanced MI detection from ECG signals.
To explore prominent dynamic features for improved accuracy in identifying MI patients.
To assess the performance of the proposed method on public and clinical datasets.

Main Methods:

Applied a dynamic learning algorithm to mine inherent dynamics in ECG signals for feature extraction.
Utilized multi-scale decomposition and dynamic modeling to represent pathological ECG changes.
Employed a hybrid feature selection algorithm to reduce the feature set, followed by classifier training and testing.

Main Results:

Achieved high accuracy (94.75%), sensitivity (94.18%), and specificity (96.33%) for MI detection on the PTB dataset.
Validated the method on an independent clinical dataset of 200 patients, yielding a maximum accuracy of 84.96%.
Demonstrated significant improvement in MI detection under the inter-patient paradigm.

Conclusions:

The proposed method effectively extracts distinctive dynamic features from ECG signals for MI diagnosis.
The dynamic learning approach shows potential as an effective auxiliary tool for diagnosing MI patients.
Experimental results support the clinical utility of this advanced feature extraction technique.