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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...

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...

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Related Experiment Video

Updated: Jun 6, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Reducing electrocardiographic artifacts from electromyogram signals with independent component analysis.

J D Costa Junior¹, D D Ferreira, J Nadal

¹Biomedical Engineering Program, Federal University of Rio de Janeiro, P. O. Box 68510, ZIP 21941-972, Brazil. dilermando@peb.ufrj.br

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

|November 25, 2010

Summary

Independent component analysis (ICA) using multiple lumbar electromyogram (EMG) signals failed to remove ECG artifacts. However, a single-channel EMG approach with time-shifted data successfully reduced ECG noise in EMG signals.

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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
Neuroscience

Background:

Surface electromyogram (EMG) signals are crucial for assessing muscle activity.
Electrocardiogram (ECG) artifacts can contaminate EMG recordings, particularly from deep muscles like the lumbar erector spinae.
Artifact reduction is essential for accurate EMG analysis.

Purpose of the Study:

To reduce ECG artifacts from lumbar surface EMG signals.
To evaluate the effectiveness of blind source separation using independent component analysis (ICA).

Main Methods:

Utilized independent component analysis (ICA), specifically the FastICA algorithm.
Applied the method to four-channel EMG signals from lumbar erector spinae muscles of 27 subjects.
Investigated a modified approach using a single EMG channel with a time-shifted version of itself.

Main Results:

The standard ICA method failed to effectively separate ECG artifacts when applied to multi-channel lumbar EMG data.
The modified FastICA approach, using a single channel and a time-shifted signal, demonstrated a reduction in the ECG noise-to-signal ratio.
This suggests a potential for artifact reduction in specific EMG recording configurations.

Conclusions:

Multi-channel ICA is not always effective for removing ECG artifacts from lumbar EMG.
A single-channel, time-shifted ICA approach shows promise for reducing ECG contamination in EMG.
Further research may optimize this technique for clinical and research applications.