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

1.6K
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...
1.6K
Mechanism of Cardiac Arrhythmias01:28

Mechanism of Cardiac Arrhythmias

847
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.
847
Disturbances in Heart Rhythm01:28

Disturbances in Heart Rhythm

699
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...
699
ECG Interpretation of Arrhythmias I: Sinus Arrhythmias01:16

ECG Interpretation of Arrhythmias I: Sinus Arrhythmias

149
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,...
149
Increased pulse rate01:17

Increased pulse rate

620
Tachycardia is a condition marked by an abnormally fast or irregular heart rate, surpassing the typical resting rate. In adults, tachycardia is characterized by a pulse rate ranging from 100 to 180 beats per minute. The increased heart rate can result in inadequate blood flow to various body parts, ultimately diminishing the oxygen supply to organs and tissues.
Many factors can elevate the risk of developing tachycardia. These include advanced age, a family history of arrhythmias, and an...
620
Pulse rhythm01:30

Pulse rhythm

717
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...
717

You might also read

Related Articles

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

Sort by
Same author

AI-assisted diagnosis of cervical dysplasia from cervicography images.

Scientific reports·2026
Same author

Computer-aided assessment for enlarged fetal heart with deep learning model.

iScience·2025
Same author

A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making.

Journal of imaging·2024
Same author

An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG).

PloS one·2024
Same author

Automatic echocardiographic anomalies interpretation using a stacked residual-dense network model.

BMC bioinformatics·2023
Same author

Accurate Prediction of Sudden Cardiac Death Based on Heart Rate Variability Analysis Using Convolutional Neural Network.

Medicina (Kaunas, Lithuania)·2023
Same journal

Risk prediction of sepsis-associated acute kidney injury: development, validation of a machine learning model with multicenter data.

BMC medical informatics and decision making·2026
Same journal

Trajectory analysis of sleep disorders and anxiety-depression in female breast cancer patients undergoing chemotherapy: based on group-based Multi-Trajectory Model and machine learning.

BMC medical informatics and decision making·2026
Same journal

Multitask learning of longitudinal circulating biomarkers and clinical outcomes: identification of optimal machine-learning and deep-learning models.

BMC medical informatics and decision making·2026
Same journal

Comparative machine learning approaches to prognosticate clinical outcomes in oral and maxillofacial space infections: a retrospective analysis.

BMC medical informatics and decision making·2026
Same journal

Development and validation of machine learning models for early diagnosis of hemophagocytic lymphohistiocytosis in pediatric Epstein-Barr virus infection.

BMC medical informatics and decision making·2026
Same journal

Clinical subphenotypes in septic patients with new-onset atrial fibrillation: validation and parsimonious classifier model development.

BMC medical informatics and decision making·2026
See all related articles

Related Experiment Video

Updated: May 7, 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

3.5K

An improved electrocardiogram arrhythmia classification performance with feature optimization.

Annisa Darmawahyuni1,2, Siti Nurmaini3, Bambang Tutuko2

  • 1Faculty of Engineering, Universitas Sriwijaya, Palembang, 30139, Indonesia.

BMC Medical Informatics and Decision Making
|December 30, 2024
PubMed
Summary
This summary is machine-generated.

Shallow feature extraction and metaheuristic optimization achieve 100% accuracy in electrocardiography (ECG) arrhythmia classification. This method effectively identifies critical ECG features for precise diagnosis.

Keywords:
ArrhythmiaElectrocardiogram signalsFeature extractionFeature selectionMachine learning

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

173
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

8.5K

Related Experiment Videos

Last Updated: May 7, 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

3.5K
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

173
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

8.5K

Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Data Science

Background:

  • Electrocardiography (ECG) data analysis for arrhythmia classification faces challenges due to large datasets and numerous potential features.
  • Increasing complexity of clinical symptoms necessitates efficient feature identification to avoid misclassification.

Purpose of the Study:

  • To identify optimal features for accurate ECG arrhythmia classification.
  • To evaluate shallow and deep feature extraction techniques combined with metaheuristic optimization.

Main Methods:

  • Applied shallow and deep feature extraction techniques to ECG signals.
  • Utilized a metaheuristic optimization algorithm for feature selection post-extraction.

Main Results:

  • Shallow feature extraction (time-domain analysis) with metaheuristic optimization outperformed other methods.
  • Selection of 1-3 RR-interval features achieved 100% accuracy, sensitivity, specificity, and precision.

Conclusions:

  • The proposed end-to-end architecture is simple and has low complexity.
  • This approach is highly effective for practical ECG arrhythmia classification applications.