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...

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...

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...

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...

You might also read

Related Articles

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

Sort by

Same author

A dictionary learning approach for spatio-temporal characterization of absence seizures.

Physiological measurement·2019

Same author

A closed-form unsupervised geometry-aware dimensionality reduction method in the Riemannian Manifold of SPD matrices.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2017

Same author

Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps.

Brain topography·2016

Same author

Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016

Same author

From molecular model to sparse representation of chromatographic signals with an unknown number of peaks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016

Same author

Coupled Hidden Markov Model-Based Method for Apnea Bradycardia Detection.

IEEE journal of biomedical and health informatics·2015

Search research articles

Related Experiment Video

Updated: Jul 17, 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

Filtering electrocardiogram signals using the extended Kalman filter.

R Sameni¹, M B Shamsollahi, C Jutten

¹School of Electrical Engineering, Sharif University of Technology, Tehran, Iran.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 7, 2007

Summary

The Extended Kalman Filter (EKF) effectively filters Electrocardiogram (ECG) signals. This powerful tool extracts vital cardiac data from noisy measurements, advancing noninvasive fetal monitoring.

More Related Videos

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

Related Experiment Videos

Last Updated: Jul 17, 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

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

Area of Science:

Biomedical Engineering
Signal Processing
Cardiology

Background:

Electrocardiogram (ECG) signal analysis is crucial for diagnosing cardiac conditions.
Noise in ECG recordings significantly hinders accurate interpretation.
Noninvasive fetal cardiac signal extraction presents unique challenges due to signal attenuation and maternal interference.

Purpose of the Study:

To evaluate the Extended Kalman Filter (EKF) as a method for filtering and extracting ECG signals.
To assess the efficacy of EKF in processing synthetic ECG signals generated by a nonlinear dynamic model.
To determine the potential of EKF for real-world applications, particularly in noninvasive fetal ECG monitoring.

Main Methods:

Utilized a previously developed nonlinear dynamic model for synthetic ECG signal generation.
Applied the Extended Kalman Filter (EKF) algorithm to denoise and extract ECG signals from simulated noisy data.
Validated the filtering performance using the generated synthetic ECG signals.

Main Results:

The EKF demonstrated significant capability in filtering noisy ECG signals.
The algorithm successfully extracted underlying ECG components from simulated measurements.
Quantitative and qualitative assessments confirmed the effectiveness of the EKF approach.

Conclusions:

The Extended Kalman Filter (EKF) is a powerful tool for ECG signal extraction from noisy data.
EKF shows promise for advancing noninvasive cardiac monitoring, especially for fetal ECG.
This method represents a state-of-the-art approach for signal processing in biomedical applications.