Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Holter Monitor: 24-Hour Monitoring

Holter Monitor: 24-Hour Monitoring

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...

Electrocardiogram

Electrocardiogram

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 the T...

Pulse rhythm

Pulse rhythm

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 muscle...

Electrocardiogram Fundamentals

Electrocardiogram Fundamentals

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 to...

ECG Interpretation of Rhythms

ECG Interpretation of Rhythms

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. When...

Dysrhythmias V: Evaluating Dysrhythmias

Dysrhythmias V: Evaluating Dysrhythmias

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...

You might also read

Related Articles

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

Sort by

Same author

Real-world evidence for passive video-based cardiac monitoring from smartphones used by patients with a history of AF.

Journal of electrocardiology·2025

Same author

Measurements of pulse rate using facial video cameras from smart devices in patients diagnosed with atrial fibrillation.

Journal of electrocardiology·2023

Same author

Can adenosine triphosphate be a proxy measure in evaluation of hand disinfection effect?

The Journal of hospital infection·2020

Same author

Cardiac repolarization and depolarization in people with Type 1 diabetes with normal ejection fraction and without known heart disease: a case-control study.

Diabetic medicine : a journal of the British Diabetic Association·2018

Same author

Commentary on: "Levofloxacin-Induced QTc Prolongation Depends on the Time of Drug Administration".

CPT: pharmacometrics & systems pharmacology·2016

Same author

Assessing ECG signal quality indices to discriminate ECGs with artefacts from pathologically different arrhythmic ECGs.

Physiological measurement·2016

Search research articles

Related Experiment Video

Updated: May 30, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

A Wavelet-Based Algorithm for Delineation and Classification of Wave Patterns in Continuous Holter ECG Recordings.

L Johannesen¹, Usl Grove, Js Sørensen

¹Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.

Computing in Cardiology

|July 23, 2011

Summary

This study introduces a wavelet-based classifier for electrocardiogram (ECG) waveforms, achieving high accuracy in identifying P-waves, QRS complexes, and T-waves. This method enables automated waveform classification in long-term ECG monitoring.

Related Experiment Videos

Last Updated: May 30, 2026

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG

Published on: March 10, 2017

Area of Science:

Biomedical Engineering
Signal Processing
Cardiology

Background:

Accurate quantitative analysis of electrocardiogram (ECG) signals is crucial for diagnosing cardiac conditions.
Automated delineation and classification of individual ECG wave patterns (P-wave, QRS complex, T-wave, U-wave) remain challenging for long-term monitoring.

Purpose of the Study:

To develop and validate a novel wavelet-based classifier for ECG waveforms.
To assess the accuracy of classifying individual ECG wave patterns using identified fiducial points.

Main Methods:

A wavelet-based waveform classifier was developed, utilizing fiducial points identified by an automated delineation algorithm.
The algorithm was validated using manually annotated ECG records from the Physionet QT database.

Main Results:

The proposed classifier achieved high classification accuracies: 85.6% for P-wave, 89.7% for QRS complex, and 92.8% for T-wave.
A 76.9% accuracy was obtained for U-wave classification.
The results demonstrate the feasibility of classifying ECG waveforms based on delineated fiducial points.

Conclusions:

The wavelet-based approach provides an effective method for automatic ECG waveform classification.
This technique is suitable for classifying wave patterns in continuous, long-term ECG recordings, such as 24-hour Holter monitoring.