Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Recurrent and metastatic osteoclast-like giant cell tumor of the liver revealed by FDG PET/CT.

Clinical nuclear medicine·2012

Same author

Case-control study of single nucleotide polymorphisms of PSCA and MUC1 genes with gastric cancer in a Chinese.

Asian Pacific journal of cancer prevention : APJCP·2012

Same author

Significance of Aspergillus spp. isolation from lower respiratory tract samples for the diagnosis and prognosis of invasive pulmonary aspergillosis in chronic obstructive pulmonary disease.

Chinese medical journal·2012

Same author

Stage-specific gender differences in cognitive and neuropsychiatric manifestations of vascular dementia.

American journal of Alzheimer's disease and other dementias·2012

Same author

Oncolytic virus-mediated tumor radiosensitization in mice through DNA-PKcs-specific shRNA.

Translational cancer research·2012

Same author

A label-free electrochemiluminescence aptasensor for thrombin based on novel assembly strategy of oligonucleotide and luminol functionalized gold nanoparticles.

Biosensors & bioelectronics·2012

Search research articles

Related Experiment Video

Updated: Jul 9, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

Deep learning-based regional ECG diagnosis platform.

Fang Li¹, Ping Wang¹, Xiao Wang Li¹

¹Department of Cardiology, The First Affiliated Hospital of Huzhou University, Huzhou, Zhejiang Province, China.

Pacing and Clinical Electrophysiology : PACE

|November 29, 2023

Summary

This summary is machine-generated.

A deep learning model for Electrocardiogram (ECG) diagnosis shows high accuracy for common conditions. This AI system assists physicians and reduces labor costs in ECG analysis.

Keywords:

ECG diagnosis deep learning diagnostic system electrocardiogram the neural network

More Related Videos

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

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

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

Published on: December 11, 2019

Related Experiment Videos

Last Updated: Jul 9, 2025

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

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

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

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

Published on: December 11, 2019

Area of Science:

Cardiology
Artificial Intelligence
Medical Diagnostics

Background:

Electrocardiograms (ECGs) are crucial for diagnosing heart conditions.
Accurate and efficient ECG interpretation is essential for patient care.
Traditional ECG analysis can be time-consuming and prone to human error.

Purpose of the Study:

To develop and evaluate a deep learning-based system for intelligent ECG diagnosis.
To assess the system's performance against manual interpretation methods.
To explore the clinical utility of an AI-driven ECG diagnostic tool.

Main Methods:

A deep learning model was constructed using a dataset of 100,120 conventional 12-lead ECGs (2015-2019).
A multi-task learning framework was employed for diagnosing prevalent ECG anomalies.
Model performance was validated through real-time analysis of 2500 ECGs and compared to manual identification.

Main Results:

The deep learning model achieved high efficacy (F1 scores >91%) for common ECG patterns like sinus rhythm and normal electrocardiograms.
The system demonstrated superior efficiency in accuracy, labor time, and cost compared to manual ECG identification.
Further refinement is needed for diagnosing rarer and more complex ECG anomalies.

Conclusions:

The deep learning-driven intelligent ECG diagnostic model possesses significant clinical utility.
This AI system can augment physicians' diagnostic capabilities.
The integrated system offers a potential solution for reducing labor costs in ECG analysis.