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

Pulse rhythm01:30

Pulse rhythm

1.5K
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
1.5K
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

3.3K
Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
3.3K
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

422
Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
422
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

13.6K
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...
13.6K
Disturbances in Heart Rhythm01:29

Disturbances in Heart Rhythm

3.3K
Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
Arrhythmias are categorized by their speed, rhythm, and origin. A slow heart...
3.3K
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

2.4K
Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
2.4K

You might also read

Related Articles

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

Sort by
Same author

The Alberta Quality Assessment Tool: Risk of Bias (AQAT:RoB) for the Evaluation of Medical Large Language Model Question-Answer Studies: Development and Pilot Validation.

Journal of medical Internet research·2026
Same author

Automatic Bone Segmentation from MRI for Real-Time Knee Tracking in Fluoroscopic Imaging.

Diagnostics (Basel, Switzerland)·2022
Same author

Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features.

Sensors (Basel, Switzerland)·2022
Same author

An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals.

Sensors (Basel, Switzerland)·2021
Same author

Usability and Acceptability of a Home Blood Pressure Telemonitoring Device Among Community-Dwelling Senior Citizens With Hypertension: Qualitative Study.

JMIR aging·2019
Same author

Telemonitoring and Protocolized Case Management for Hypertensive Community-Dwelling Seniors With Diabetes: Protocol of the TECHNOMED Randomized Controlled Trial.

JMIR research protocols·2016

Related Experiment Video

Updated: Feb 28, 2026

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

4.5K

From Motion Artifacts to Clinical Insight: Multi-Modal Deep Learning for Robust Arrhythmia Screening in Ambulatory

Pierre Boulanger1

  • 1Department of Computing Science, University of Alberta, Edmonton, AB T6G 2R3, Canada.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

This study introduces a deep learning framework to improve arrhythmia detection from wearable ECGs, significantly reducing false alarms caused by motion. The system offers robust, real-time cardiac monitoring for better patient care.

Keywords:
MIT-BIHaccelerometerarrhythmia detectiondeep learningfalse alarm reductionmotion artifactssensor fusionwearable 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

9.2K
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

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

Published on: April 11, 2025

2.0K

Related Experiment Videos

Last Updated: Feb 28, 2026

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

4.5K
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

9.2K
Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
10:17

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

Published on: April 11, 2025

2.0K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Signal Processing

Background:

  • Motion artifacts in wearable electrocardiogram (ECG) signals are a major challenge for continuous cardiac monitoring.
  • These artifacts lead to false arrhythmia alarms, hindering clinical adoption and reliable patient assessment.

Purpose of the Study:

  • To develop a motion-robust deep learning framework for binary arrhythmia classification using multi-modal sensor fusion.
  • To enhance the reliability of continuous cardiac monitoring systems by mitigating motion-related signal corruption.

Main Methods:

  • A dual-stream deep learning architecture integrating ResNet-18 for ECG spectrograms and CNN-BiLSTM for accelerometer data.
  • Attention-gated fusion with gate diversity regularization for adaptive multi-modal weighting based on signal quality.
  • Multi-Signal-to-Noise Ratio (SNR) training using augmented MIT-BIH data at varying noise levels (24, 12, 6 dB) for noise-invariant learning.

Main Results:

  • Achieved 99.5% accuracy on clean signals, with performance gracefully degrading to 88.2% at -6 dB SNR (extreme noise).
  • Demonstrated a 46% improvement compared to single-SNR training, highlighting the benefit of multi-SNR learning.
  • Confirmed adaptive, context-dependent fusion through high gate diversity (σ>0.37).
  • Achieved a low false positive rate (0.09%) and real-time processing (238 beats/second).

Conclusions:

  • The proposed framework offers practical, continuous arrhythmia screening with high accuracy and robustness to motion artifacts.
  • Establishes a foundation for hierarchical monitoring systems, enabling efficient binary screening before detailed multi-class diagnosis.
  • Paves the way for wider clinical adoption of wearable continuous cardiac monitoring devices.