Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.0K
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...
1.0K
Electrocardiogram01:29

Electrocardiogram

4.3K
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...
4.3K

You might also read

Related Articles

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

Sort by
Same author

Optimized Signal Acquisition and Advanced AI for Robust 1D EMG Classification: A Comparative Study of Machine Learning, Deep Learning, and Reinforcement Learning.

Bioengineering (Basel, Switzerland)·2026
Same author

A Comparative Evaluation of Strain and Shear Wave Ultrasound Elastography for Characterizing Cervical Lymphadenopathy.

Cureus·2026
Same author

Immediate complete denture rehabilitation using combined digital-functional impression technique and three-dimensional-printed occlusal templates.

Journal of Indian Prosthodontic Society·2025
Same author

Harnessing The Role Of Rubiadin in Cancer: Current Scenario And Future Perspectives.

Fitoterapia·2025
Same author

A Case Study Based on a Complementary and Alternative Medicine Approach on an Oligoasthenoteratozoospermic Patient and its Effect on Assisted Reproductive Technology Outcome.

Journal of pharmacy & bioallied sciences·2024
Same author

Validation of effect of composite additive on optimized combustion characteristics of CI engine using AHP and GRA method.

Heliyon·2024

Related Experiment Video

Updated: Nov 1, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

4.0K

ECG Paper Record Digitization and Diagnosis Using Deep Learning.

Siddharth Mishra1, Gaurav Khatwani1, Rupali Patil1

  • 1K. J. Somaiya College of Engineering, Vidyavihar, Mumbai India 400077.

Journal of Medical and Biological Engineering
|June 21, 2021
PubMed
Summary
This summary is machine-generated.

Digitizing paper electrocardiogram (ECG) records using deep learning enables automated heart problem diagnosis. This method achieves high accuracy in converting ECG images to digital signals and identifying conditions like STEMI.

Keywords:
Deep learningDiagnosisDigitizationPaper ECG

More Related Videos

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

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

Published on: December 11, 2019

8.9K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.4K

Related Experiment Videos

Last Updated: Nov 1, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
06:07

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

Published on: May 23, 2021

4.0K
Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

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

Published on: December 11, 2019

8.9K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.4K

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Electrocardiogram (ECG) is crucial for heart problem detection.
  • Manual assessment of paper ECG records is time-consuming.
  • Digitizing paper ECGs enables automated analysis.

Purpose of the Study:

  • To develop a deep learning model for converting paper ECG records into 1-D digital signals.
  • To achieve accurate automated diagnosis of heart diseases from digitized ECGs.

Main Methods:

  • Utilized camera-captured or scanned ECG paper images.
  • Applied pre-processing for shadow removal and deep learning for signal binarization (97% accuracy).
  • Employed another deep learning model for diagnosing heart conditions (e.g., STEMI, LBBB, RBBB) from digitized signals (94.4% accuracy).

Main Results:

  • Achieved 97% accuracy in deep learning-based binarization of ECG images.
  • Attained 94.4% accuracy in diagnosing heart diseases from digitized paper ECG records.

Conclusions:

  • Digitized ECG signals facilitate trend analysis and diagnosis from historical records.
  • This approach is valuable for research and can be implemented in areas lacking expert availability.