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

Instrumentation Amplifier01:25

Instrumentation Amplifier

1.0K
An electrocardiography (ECG) machine is an essential piece of medical equipment used to monitor the electrical activity of the heart. It operates by detecting small electrical changes on the skin that result from the depolarization of the heart muscle during each heartbeat. However, these signals are in the microvolt range and can be easily overwhelmed by noise or interference.
To overcome this challenge, an ECG machine utilizes an instrumentation amplifier. This specialized amplifier is...
1.0K
Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

1.4K
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...
1.4K
Electrocardiogram01:29

Electrocardiogram

5.4K
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...
5.4K
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

12.6K
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....
12.6K
Dysrhythmias V: Evaluating Dysrhythmias01:30

Dysrhythmias V: Evaluating Dysrhythmias

328
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...
328
ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias01:25

ECG Interpretation of Arrhythmias II: Atrial, Junctional and Ventricular Arrhythmias

465
Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
465

You might also read

Related Articles

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

Sort by
Same author

A hybrid approach for machine learning based beat classification of ECG using different digital differentiators and DTCWT.

Computers in biology and medicine·2025
See all related articles

Related Experiment Video

Updated: Jan 15, 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

4.3K

ECG beat classification with fractional order differentiator and machine learning techniques.

H K Prasad Katamreddi1, Tirumala Krishna Battula1

  • 1Department of Electronics and Communication Engineering, Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh, India.

Biomedical Physics & Engineering Express
|October 7, 2025
PubMed
Summary
This summary is machine-generated.

Automated electrocardiogram (ECG) analysis improves heart disease detection. A new method using fractional order differentiation and DTCWT features with machine learning achieves high accuracy in classifying ECG beats.

Keywords:
ECG Beatsclassificationfractional order differentiatormachine learning

More Related Videos

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.0K
Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
04:45

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

2.9K

Related Experiment Videos

Last Updated: Jan 15, 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

4.3K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.0K
Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice
04:45

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

2.9K

Area of Science:

  • Biomedical Engineering
  • Cardiology
  • Signal Processing

Background:

  • Manual electrocardiogram (ECG) analysis is laborious and prone to errors.
  • Automated ECG analysis is crucial for early cardiovascular disease detection, especially with irregular heartbeats.
  • Accurate identification of abnormal heartbeats is essential for timely diagnosis and treatment.

Purpose of the Study:

  • To develop a novel, accurate approach for automated ECG beat classification.
  • To integrate fractional order differentiation, dual-tree complex wavelet transform (DTCWT) features, and machine learning (ML) for enhanced ECG analysis.
  • To improve the diagnostic accuracy of ECG interpretation for better clinical outcomes.

Main Methods:

  • R-peak detection was performed using a fractional order differentiator.
  • Feature extraction was conducted using the dual-tree complex wavelet transform (DTCWT).
  • Various machine learning (ML) classifiers were employed for ECG beat classification, including Random Forest.

Main Results:

  • The proposed methodology demonstrated superior performance on the MIT-BIH Arrhythmia Database.
  • The Random Forest classifier achieved high accuracy (96.82%), sensitivity (96.83%), specificity (97.02%), PPV (96.89%), and F1-score (96.85%).
  • The integrated approach effectively handles signal irregularity and non-stationarity in ECG data.

Conclusions:

  • The proposed method significantly enhances the accuracy of ECG beat classification.
  • This approach contributes to more reliable early detection of cardiovascular diseases.
  • Improved ECG analysis accuracy can lead to better clinical decision-making and patient outcomes.