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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...

Electrocardiogram

Electrocardiogram

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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Related Experiment Video

Updated: Jul 21, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm.

Annisa Darmawahyuni¹, Siti Nurmaini², Muhammad Naufal Rachmatullah¹

¹Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia.

BMC Medical Informatics and Decision Making

|July 28, 2023

Summary

This study introduces a deep learning algorithm for automated 12-lead electrocardiogram (ECG) delineation, achieving over 95% accuracy. This advancement simplifies the analysis of complex ECG signals in clinical practice.

Keywords:

12-lead electrocardiogram Bidirectional long short-term memory Convolutional neural network Delineation model ECG waveform Isoelectric line

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Last Updated: Jul 21, 2025

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Area of Science:

Cardiology
Artificial Intelligence
Signal Processing

Background:

Manual delineation of 12-lead electrocardiogram (ECG) signals is challenging due to signal variations, noise, and irregular heart rhythms.
Accurate ECG signal delineation is crucial for extracting comprehensive information and characteristics in clinical practice.

Purpose of the Study:

To develop and evaluate a deep learning algorithm for automated delineation of 12-lead ECG signals.
To classify ECG waveforms and boundaries, including P-wave, QRS-complex, and T-wave.

Main Methods:

Implemented a deep learning model utilizing convolutional layers within convolutional neural networks (CNNs) for automated feature extraction.
Employed a bidirectional long short-term memory (BiLSTM) network as a classifier.
Experimented with both beat-based and patient-based approaches for ECG beat segmentation.

Main Results:

The proposed deep learning model achieved excellent performance across all metrics.
Achieved over 95% accuracy for beat-based segmentation and over 93% accuracy for patient-based segmentation.
Evaluated on a dataset of 14,588 ECG beats.

Conclusions:

The developed automated 12-lead ECG delineation model demonstrates high accuracy and efficiency.
This deep learning approach represents a significant advancement towards clinical application in cardiology.
The model's performance indicates its potential to aid clinicians in ECG interpretation.