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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

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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.
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An ECG utilizes electrodes on the skin...

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Related Experiment Video

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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Published on: May 23, 2021

A One-Dimensional Siamese Few-Shot Learning Approach for ECG Classification under Limited Data.

Zongjin Li, Huan Wang, Xinwen Liu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|December 11, 2021

Summary

This study introduces a few-shot learning method using Siamese networks for electrocardiogram (ECG) classification. The approach effectively addresses limited labeled data challenges in medical signal analysis, outperforming existing methods.

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Area of Science:

Cardiology
Artificial Intelligence
Machine Learning

Background:

Electrocardiogram (ECG) analysis is crucial for diagnosing cardiac arrhythmias.
Deep learning models achieve high accuracy in ECG classification but require substantial labeled data.
Scarcity of labeled medical signals hinders deep learning model performance.

Purpose of the Study:

To propose a few-shot learning approach for ECG classification to overcome data scarcity.
To develop a Siamese network-based method for improved ECG analysis with limited samples.

Main Methods:

Utilized a Siamese network architecture with two shared-weight 1D convolutional neural networks (CNNs).
Extracted feature vectors from paired ECG signals.
Calculated L1-distance between feature vectors and used a sigmoid activation in a fully connected layer for classification.

Main Results:

The proposed few-shot ECG classification method demonstrated superior performance compared to existing networks.
Experiments were validated on the MIT-BIH arrhythmia database.
The method proved effective even with extremely limited data.

Conclusions:

The Siamese network-based few-shot learning approach effectively addresses the challenge of limited labeled data in ECG classification.
This method offers a promising solution for developing accurate arrhythmia diagnostic tools in data-scarce medical environments.