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Electrocardiogram Fundamentals

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...

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Updated: Dec 26, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

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Non-Standardized Patch-Based ECG Lead Together With Deep Learning Based Algorithm for Automatic Screening of Atrial

Dakun Lai, Yuxiang Bu, Ye Su

IEEE Journal of Biomedical and Health Informatics

|March 17, 2020

Summary

This study explored using a modified single-lead electrocardiogram (ECG) patch and deep learning to detect atrial fibrillation (AF). The optimized approach demonstrated high accuracy for AF screening, suggesting a potential low-cost clinical tool.

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Last Updated: Dec 26, 2025

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High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

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Published on: July 29, 2011

Area of Science:

Cardiology
Biomedical Engineering
Artificial Intelligence

Background:

Atrial fibrillation (AF) detection often relies on standard 12-lead electrocardiograms (ECG) or Holter monitoring.
Non-standardized, single-lead ECG monitoring offers potential for more accessible AF screening.
Integrating deep learning algorithms with novel ECG lead configurations is an emerging area for improved diagnostic capabilities.

Purpose of the Study:

To evaluate the feasibility of a non-standardized, single-lead ECG monitoring system for automated atrial fibrillation (AF) detection.
To assess the performance of deep learning algorithms combined with a modified patch-based ECG lead for AF diagnosis.
To determine the optimal lead position on the chest for accurate AF detection using patch-based ECG.

Main Methods:

Fifty-five patients underwent 24-hour monitoring using patch-based ECG devices and a standard 12-lead Holter.
Four distinct positions for the patch lead on the upper-left chest were investigated.
Automated AF detection algorithms, including four convolutional neural networks (CNNs), were evaluated against clinician annotations for each patch lead position.

Main Results:

A total of 349,388 AF segments and 161,084 sinus rhythm segments were analyzed.
The modified lead II position (MP1) on the patch-based ECG showed good agreement with the standard 12-lead recordings.
An R-R interval-based CNN model achieved high performance on the MP1 lead: 93.1% accuracy, 93.1% sensitivity, and 93.4% specificity for AF detection.

Conclusions:

An optimized patch-based single-lead ECG, particularly at the modified lead II position, is feasible for AF detection.
Deep learning algorithms, specifically CNNs analyzing R-R intervals, demonstrate promising performance in identifying AF.
This combined approach may provide an accurate, user-friendly, and cost-effective tool for mass AF screening.