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Related Concept Videos

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...
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Correlation between ECG and Cardiac Cycle01:25

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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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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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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Instrumentation Amplifier01:25

Instrumentation Amplifier

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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...
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Related Experiment Video

Updated: Jan 9, 2026

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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Lightweight Data-driven ECG Classification Approach with Explainable CAM Output.

Rytis Augustauskas, Ana Santos Rodrigues, Daivaras Sokas

    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.

    This study introduces a lightweight AI model for classifying electrocardiogram (ECG) signals, offering enhanced explainability through Class Activation Maps (CAMs) with minimal computational overhead.

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    Area of Science:

    • Artificial Intelligence in Healthcare
    • Biomedical Signal Processing
    • Explainable AI (XAI)

    Background:

    • Electrocardiogram (ECG) signal analysis is crucial for cardiac diagnostics.
    • Current AI models often lack transparency, hindering clinical trust.
    • Need for efficient and interpretable AI solutions in real-time healthcare.

    Purpose of the Study:

    • To develop a lightweight, data-driven AI model for binary ECG classification (normal vs. disease).
    • To integrate explainability using a non-trainable Class Activation Map (CAM) without significant computational increase.
    • To enhance clinical interpretability and trust in AI-driven cardiac diagnostics.

    Main Methods:

    • Utilized a minimalistic Convolutional Neural Network (CNN) architecture.
    • Implemented a non-trainable Class Activation Map (CAM) for generating decision-making heatmaps.
    • Employed data preprocessing including detrending, standardization, and augmentation on the PTB-XL dataset.

    Main Results:

    • Achieved 85.4% accuracy and 0.93 Area Under the ROC Curve (AUC) on the PTB-XL dataset.
    • The CAM integration added only 14.99% computational complexity.
    • The model requires minimal parameters (22,273) and FLOPs (92,064).

    Conclusions:

    • The proposed lightweight CNN with CAM offers efficient and interpretable ECG classification.
    • This approach facilitates trustworthy AI deployment for real-time cardiac anomaly detection.
    • Demonstrates the feasibility of integrating explainable AI into clinical decision-making frameworks.