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Electrocardiogram01:29

Electrocardiogram

7.6K
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...
7.6K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.9K
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...
1.9K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

18.0K
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....
18.0K

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Related Experiment Video

Updated: Mar 26, 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

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Electrocardiogram ST-Segment Morphology Delineation Method Using Orthogonal Transformations.

Miha Amon1, Franc Jager1

  • 1Faculty of Computer and Information Science, University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia.

Plos One
|February 11, 2016
PubMed
Summary

This study introduces novel orthogonal transformation methods to accurately differentiate between ischemic and non-ischemic transient ST segment changes in long-term ECGs. These advanced techniques improve automated detection of myocardial ischemia, enhancing diagnostic capabilities.

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Distinguishing ischemic from non-ischemic transient ST segment events in long-term ambulatory electrocardiograms remains a challenge for current detection systems.
  • Traditional ST segment analysis relies on single-point measurements, which are imprecise and susceptible to noise, limiting diagnostic accuracy.

Purpose of the Study:

  • To develop a robust, noise-resistant method for delineating ST segment morphology changes.
  • To create feature-vector time series for analyzing transient ST segment morphology.
  • To evaluate the classification power of new transformation-based feature vectors for differentiating ischemic and non-ischemic events.

Main Methods:

  • Developed a noise-resistant orthogonal-transformation based delineation method for ST segment morphology.
  • Introduced a new Legendre Polynomials based Transformation (LPT) for ST segment analysis.
  • Generated Karhunen and Loève Transformation (KLT) ST segment basis functions using data from the Long Term ST Database (LTST DB).

Main Results:

  • The KLT and LPT methods effectively represent transient ST segment morphology categories.
  • Classification accuracy reached 90% with KLT and 82% with LPT using a k-Nearest Neighbors classifier.
  • New feature-vector time series were derived and contributed to the LTST DB.

Conclusions:

  • The KLT and LPT transformations offer improved capabilities for automated ischemia detection.
  • These methods provide new avenues for both human expert diagnostics and automated analysis of transient ST segment changes.
  • The developed techniques enhance the precision and robustness of ischemia detection systems.