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Related Concept Videos

Electrocardiogram Fundamentals01:28

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...
Electrocardiogram01:29

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 Rhythms01:24

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...
Correlation between ECG and Cardiac Cycle01:25

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...
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
Bode Plots Construction01:24

Bode Plots Construction

The Bode plot is an essential tool in control system analysis, mapping the frequency response of a system through a magnitude plot and a phase plot, both against a logarithmic frequency axis. To construct a Bode plot, consider the transfer function H(ω):

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Related Experiment Video

Updated: Jun 10, 2026

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

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

ST shape classification in ECG by constructing reference ST set.

Gu-Young Jeong1, Kee-Ho Yu, Myoung-Jong Yoon

  • 1Department of Biomedical Engineering, Chonbuk National University, Republic of Korea.

Medical Engineering & Physics
|August 7, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for classifying ST segment morphology in electrocardiograms (ECGs) to detect myocardial ischemia. The method achieves 83.14% accuracy, offering insights into ST shape changes crucial for diagnosis.

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Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Last Updated: Jun 10, 2026

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

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

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Medical Signal Processing

Background:

  • Abnormal ST segment changes in electrocardiograms (ECGs) are critical for diagnosing myocardial ischemia.
  • Long-term ECG monitoring is often necessary due to the transient nature of ST segment changes.
  • ST segment deviations are frequently associated with underlying myocardial abnormalities.

Purpose of the Study:

  • To develop and evaluate an algorithm for classifying ST segments based on their morphology.
  • To provide a method for analyzing changes in ST segment shape over time.
  • To establish a classification system using a defined set of reference ST shapes.

Main Methods:

  • Developed an ECG analysis algorithm incorporating feature point detection (S wave and J-point).
  • Implemented ST shape classification using a predefined set of reference ST shapes.
  • Enhanced classification performance by incorporating rules based on previous beat trends and shapes.

Main Results:

  • The proposed algorithm achieved a global correct classification rate of 83.14% for ST segments by morphology.
  • The algorithm provides information regarding changes in ST shape, a feature not present in existing ST level detection algorithms.
  • While current ST level detection algorithms report higher accuracy (90%+), they do not analyze ST morphology changes.

Conclusions:

  • The developed algorithm offers a novel approach to classifying ST segments by morphology, providing valuable diagnostic information.
  • This method is significant as it addresses the gap in analyzing ST shape changes, unlike existing ST level detection techniques.
  • The classification of STs by morphology using a reference ST set is a noteworthy contribution to ECG analysis for myocardial ischemia detection.