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

ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

13.8K
An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
13.8K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

12.5K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
12.5K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

826
Arrhythmias are disturbances in the heart's rhythm that lead to abnormal heartbeats. These irregularities can originate from different parts of the heart and are classified based on their origin and nature.
Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
826
Associative Learning01:27

Associative Learning

1.3K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.3K
Purposive Learning01:22

Purposive Learning

503
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
503

You might also read

Related Articles

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

Sort by
Same author

Iridocorneal endothelial syndrome manifesting 4.5 years after toric implantable collamer lens implantation: a case report.

BMC ophthalmology·2026
Same author

Bioinspired Carbon Radical Catalysis.

Journal of the American Chemical Society·2026
Same author

DHCR7 promotes granulosa cells ferroptosis and represents a potential therapeutic target in premature ovarian insufficiency.

Biochimica et biophysica acta. Molecular cell research·2026
Same author

Proteomic and transcriptomic analyses identify the role of RBM25 in the malignant progression of glioma.

The International journal of neuroscience·2026
Same author

Association of gut microbiota and inflammatory markers with enteral nutrition intolerance in patients with early-stage moderate-to-severe intracerebral hemorrhage.

Microbiology spectrum·2026
Same author

Multi-Stage State Assessment of Breakers Based on TCWGAN-GP and XGBoost Under Insufficient Samples.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Feb 1, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K

SLPM: a lightweight deep learning model for end-to-end paper ECG digitization.

Xiankai Yu1, Jian Wu1, Jiahao Wang1

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China.

Physiological Measurement
|January 30, 2026
PubMed
Summary

We developed a Signal Location Prediction Model (SLPM) to accurately digitize paper electrocardiograms (ECGs). SLPM overcomes noise and generalization issues, offering a precise and efficient method for ECG signal extraction.

Keywords:
digitizationlightweight modelpaper ECGsignal location

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

Related Experiment Videos

Last Updated: Feb 1, 2026

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

2.6K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

10.8K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Digitizing paper electrocardiograms (ECGs) is challenging due to errors, noise, and poor generalization.
  • Existing methods struggle with complex conditions and accurate signal extraction.

Purpose of the Study:

  • To propose an end-to-end Signal Location Prediction Model (SLPM) for high-fidelity paper ECG digitization.
  • To address challenges in ECG signal segmentation, noise interference, and generalization.

Main Methods:

  • SLPM utilizes a joint classification-regression framework for precise ECG image to time-series mapping.
  • A hierarchical Squeeze-and-Excitation Bidirectional Long Short-Term Memory (SE-BiLSTM) enhances feature representation and temporal dependencies.

Main Results:

  • SLPM achieved high accuracy on single-lead datasets (PCC=0.97, SNR≈13.64 dB) even with distortions.
  • On a 12-lead dataset, SLPM attained an SNR of 14.66 dB with only 0.31 million parameters.

Conclusions:

  • SLPM demonstrates significant advantages in accuracy, efficiency, and generalization for paper ECG digitization.
  • The model represents a promising advancement for high-fidelity digitization of historical ECG data.