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

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...

Instrumentation Amplifier

Instrumentation Amplifier

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...

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...

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...

You might also read

Related Articles

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

Sort by

Same author

Frequency Band Personalization for Seizure Network Analysis in Multifocal Patients.

International journal of neural systems·2026

Same author

AI-Based Performance Analysis for Track and Field Athletes.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

AI-Driven SEEG Channel Ranking for Epileptogenic Zone Localization.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

NeuroNet-AD: A Multimodal Deep Learning Framework for Multiclass Alzheimer's Disease Diagnosis.

Bioengineering (Basel, Switzerland)·2025

Same author

MicroAIbiome: Decoding Cancer Types from Microbial Profiles Using Explainable Machine Learning.

Microorganisms·2025

Same author

MorphoITH: a framework for deconvolving intra-tumor heterogeneity using tissue morphology.

Genome medicine·2025

Same journal

Categorization and segmentation of intestinal content frames for wireless capsule endoscopy.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013

Same journal

An intelligent scoring system and its application to cardiac arrest prediction.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013

Same journal

Guest editorial: Multimedia services and technologies for e-health (MUST-EH).

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013

Same journal

Editorial: From “information technology in biomedicine” to “biomedical and health informatics”.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013

Same journal

Equipment location in hospitals using RFID-based positioning system.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2013

Same journal

Distributed system for cognitive stimulation over interactive TV.

IEEE transactions on information technology in biomedicine : a publication of the IEEE Engineering in Medicine and Biology Society·2012

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 9, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

A patient-adaptive profiling scheme for ECG beat classification.

Miad Faezipour¹, Adnan Saeed, Suma Chandrika Bulusu

¹Quality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, TX 75083, USA. mxf042000@utdallas.edu

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society

|September 4, 2010

Summary

This summary is machine-generated.

This study introduces an automated system for electrocardiogram (ECG) analysis, accurately detecting heartbeats and classifying cardiac abnormalities for improved patient monitoring in clinical and telemedicine settings.

Related Experiment Videos

Last Updated: Jun 9, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

Area of Science:

Biomedical Engineering
Signal Processing
Cardiology

Background:

Clinical and telemedicine applications require automated electrocardiogram (ECG) signal processing and heart beat classification.
Individual ECG morphologies vary, necessitating personalized cardiac profiling for accurate diagnosis and monitoring.
Existing methods may lack the adaptability required for dynamic physiological conditions.

Purpose of the Study:

To propose a patient-adaptive cardiac profiling scheme for automated ECG analysis.
To develop a novel local ECG beat classifier for individual cardiac behavior profiling.
To enable early warning flagging of abnormal cardiac behavior.

Main Methods:

Utilized a wavelet-based mechanism for precise fiducial ECG point extraction.
Implemented a novel local classifier for patient-specific normal cardiac behavior profiling.
Employed a repetition-detection concept for adaptive profiling.

Main Results:

Achieved 99.59% accuracy in beat detection.
Demonstrated high classification accuracy of 97.42% for identifying cardiac abnormalities.
Validated the technique on the MIT-BIH arrhythmia database.

Conclusions:

The proposed patient-adaptive scheme effectively automates ECG analysis and cardiac profiling.
The method accurately detects heartbeats and identifies abnormalities, crucial for arrhythmia diagnosis.
This approach offers a valuable tool for early detection of abnormal cardiac behavior in clinical and remote monitoring.