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

Electrocardiogram01:29

Electrocardiogram

7.2K
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...
7.2K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.7K
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...
1.7K
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

13.8K
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...
13.8K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

16.6K
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....
16.6K
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

990
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,...
990
Dysrhythmias III: Characteristics of Dysrhythmias01:29

Dysrhythmias III: Characteristics of Dysrhythmias

594
Dysrhythmias, also known as arrhythmias, are irregular heart rhythms that result from abnormal electrical activity in the heart, affecting its ability to circulate blood efficiently. Tachyarrhythmias, a subset of dysrhythmias, are characterized by abnormally fast heart rates exceeding 100 beats per minute. Here are some types of tachyarrhythmias with their distinct ECG features:Sinus Tachycardia:Sinus tachycardia presents a regular heart rhythm with an increased rate of 101-180 beats per...
594

You might also read

Related Articles

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

Sort by
Same author

Heart Rate Variability During Exercise-Heat Stress Following Seven Days of Passive Heat Acclimation in Older Males: A Secondary Analysis.

Medicine and science in sports and exercise·2026
Same author

Sleep Stage Classification During CPAP Therapy from CPAP-Airflow and Wearable Fingertip Signals.

Sensors (Basel, Switzerland)·2026
Same author

Phase-dependent autonomic responses to electric fan use during a prolonged 8-h heat exposure in older adults.

American journal of physiology. Regulatory, integrative and comparative physiology·2026
Same author

External validation of a fingertip wearable device for obstructive sleep apnea diagnosis and split-night tracking of CPAP treatment response.

Sleep medicine·2026
Same author

Predicting sleep state from continuous positive airway pressure flow in patients with obstructive sleep apnea.

Sleep medicine·2026
Same author

Characterizing circadian rest-activity rhythm patterns across Alzheimer's disease continuum in Down syndrome.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026

Related Experiment Video

Updated: Mar 9, 2026

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

Heart beat classification from single-lead ECG using the synchrosqueezing transform.

Christophe L Herry1, Martin Frasch, Andrew Je Seely

  • 1Ottawa Hospital Research Institute, Ottawa, ON, Canada.

Physiological Measurement
|January 6, 2017
PubMed
Summary

This study introduces a novel synchrosqueezing transform (SST) model for enhanced electrocardiogram (ECG) analysis. The method improves beat detection and abnormal rhythm classification using single-lead ECG data.

More Related Videos

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
A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

24.9K

Related Experiment Videos

Last Updated: Mar 9, 2026

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
A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis
18:11

A Research Method For Detecting Transient Myocardial Ischemia In Patients With Suspected Acute Coronary Syndrome Using Continuous ST-segment Analysis

Published on: December 28, 2012

24.9K

Area of Science:

  • Cardiovascular physiology and signal processing.
  • Biomedical engineering and computational biology.

Background:

  • Electrocardiogram (ECG) signal processing offers vital insights into cardiac function and health.
  • Single-lead ECG devices are increasingly common for ambulatory monitoring, but accurate heart rate variability (HRV) assessment relies on precise beat detection and rhythm classification, which is challenging with limited leads.
  • Existing multi-lead ECG methods are not always practical, especially for applications like fetal monitoring.

Purpose of the Study:

  • To develop and validate a novel adaptive non-harmonic model utilizing the synchrosqueezing transform (SST) for improved ECG pattern characterization.
  • To enhance heart beat detection and classification of normal versus abnormal rhythms using single-lead ECG data.
  • To demonstrate the efficacy of SST-derived features in a machine learning classifier for arrhythmia detection.

Main Methods:

  • An adaptive non-harmonic model was developed to represent the heart rate signal.
  • The synchrosqueezing transform (SST) was employed to characterize ECG patterns.
  • A support vector machine (SVM) classifier was trained and validated using SST-derived instantaneous phase, R-peak amplitudes, and R-peak to R-peak interval durations from single-lead ECG data.
  • The Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database and Association for the Advancement of Medical Instrumentation (AAMI) beat classes were utilized for training and validation.

Main Results:

  • The proposed model successfully enhanced heart beat detection and classification accuracy for normal and abnormal rhythms.
  • The SST-derived features, combined with SVM, achieved sensitivities and positive predictive values comparable to established multi-lead algorithms.
  • This demonstrates the potential of single-lead ECG analysis with advanced signal processing techniques.

Conclusions:

  • The synchrosqueezing transform (SST) provides a powerful tool for analyzing single-lead ECG signals, enabling robust beat detection and arrhythmia classification.
  • The developed adaptive non-harmonic model and SST-based feature extraction offer a viable and effective alternative to multi-lead ECG analysis for certain applications.
  • This approach holds promise for improving cardiovascular health monitoring through accessible, single-lead ambulatory devices.