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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

12.2K
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....
12.2K
Electrocardiogram01:29

Electrocardiogram

5.3K
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...
5.3K
Instrumentation Amplifier01:25

Instrumentation Amplifier

998
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
998
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.4K
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...
1.4K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

11.6K
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...
11.6K
Pulse rhythm01:30

Pulse rhythm

1.3K
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...
1.3K

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Related Experiment Video

Updated: Jan 9, 2026

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

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I-BEAT: Interpretable Transformer Model for Intra-Beat Wave Detection on Ambulatory ECG.

Carmen PlazanSeco, Mohammad Baksh, Kenneth E Barner

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    We developed I-BEAT, an interpretable AI model for accurate electrocardiogram (ECG) wave detection. This method enhances diagnosis by improving P-wave, QRS complex, and T-wave identification in noisy, real-world ambulatory settings.

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

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Medicine
    • Cardiovascular Diagnostics

    Background:

    • Accurate electrocardiogram (ECG) wave detection is crucial for diagnosing cardiac conditions.
    • Current deep learning methods lack interpretability and struggle with noise and variability in ambulatory ECG data.
    • Existing approaches often require pre-identification of heartbeats, limiting their effectiveness.

    Purpose of the Study:

    • To introduce I-BEAT, a novel interpretable transformer-based model for direct intra-beat ECG wave detection (P-waves, QRS complexes, T-waves).
    • To enhance the accuracy and clinical relevance of ECG analysis through an explainable AI approach.
    • To overcome limitations of existing methods by eliminating the need for prior heartbeat identification.

    Main Methods:

    • Developed an interpretable transformer-based model (I-BEAT) for direct detection of P-waves, QRS complexes, and T-waves.
    • Utilized manually annotated datasets (QTDB, LUDB) with strict patient separation.
    • Implemented a proximity-based labeling method to address class imbalance and artifact processing techniques for real-world ambulatory data.

    Main Results:

    • Achieved high F1-scores: 94.59% for P-waves, 98.76% for QRS complexes, and 97.53% for T-waves.
    • Demonstrated improved detection accuracy and robustness in continuous ambulatory ECG recordings.
    • Provided model interpretability through attention and saliency maps, enhancing clinical trust.

    Conclusions:

    • I-BEAT offers accurate, interpretable, and robust detection of intra-beat ECG waves, addressing key challenges in ambulatory settings.
    • The model's explainability through attention and saliency maps increases its clinical utility.
    • Represents a significant advancement towards reliable and explainable ECG analysis for improved cardiac diagnostics.