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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...

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: Jul 18, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

Robust electrocardiogram delineation model for automatic morphological abnormality interpretation.

Siti Nurmaini¹, Annisa Darmawahyuni², Muhammad Naufal Rachmatullah³

¹Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, 30139, Indonesia. siti_nurmaini@unsri.ac.id.

Scientific Reports

|August 23, 2023

Summary

This summary is machine-generated.

This study introduces a robust convolutional recurrent network model for precise electrocardiogram (ECG) P-QRS-T wave delineation, achieving over 99% accuracy. The model effectively interprets ECG abnormalities, even with noise, aiding in arrhythmia detection.

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Last Updated: Jul 18, 2025

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Area of Science:

Biomedical Engineering
Cardiology
Artificial Intelligence

Background:

Accurate electrocardiogram (ECG) wave delineation is crucial for diagnosing heart abnormalities.
Existing methods struggle with noise interference and integrating medical knowledge for accurate P-QRS-T wave identification.
Robust ECG signal processing is essential for reliable cardiac diagnostics.

Purpose of the Study:

To develop a robust delineation model for precise P-QRS-T wave classification in ECG signals.
To improve the accuracy and reliability of ECG delineation, especially in the presence of noise and artifacts.
To integrate medical knowledge for interpreting ECG morphological abnormalities and detecting arrhythmias.

Main Methods:

A convolutional recurrent network model was developed and optimized using grid search.
The model was trained and validated on multiple diverse ECG datasets (LUDB, QTDB, PhysioNet, etc.).
ECG waveform classification results were used to interpret morphological abnormalities and detect arrhythmias based on P-wave and RR interval analysis.

Main Results:

The best performing model achieved 99.97% accuracy, 99.92% sensitivity, and 99.93% precision for ECG waveform classification.
The model demonstrated robustness across seven different ECG datasets, handling artifact noise and baseline drift effectively.
The proposed method successfully interpreted delineation results for arrhythmia detection, considering P-wave morphology and RR interval regularity.

Conclusions:

The proposed convolutional recurrent network model offers a robust and accurate solution for ECG delineation.
The model's ability to integrate medical knowledge enhances its capability in identifying cardiac abnormalities and arrhythmias.
This approach provides a significant advancement in automated ECG analysis for clinical applications.