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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...

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...

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...

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Related Experiment Video

Updated: Oct 22, 2025

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

Published on: May 5, 2022

Electrocardiogram Quality Assessment Using Unsupervised Deep Learning.

Nick Seeuws, Maarten De Vos, Alexander Bertrand

IEEE Transactions on Bio-Medical Engineering

|August 30, 2021

Summary

This summary is machine-generated.

A new data-driven method using deep learning effectively identifies clean electrocardiogram (ECG) signals, outperforming traditional indicators. This approach removes reliance on human-defined signal properties for improved ECG processing.

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Related Experiment Videos

Last Updated: Oct 22, 2025

Real-Time Electrocardiogram Monitoring During Treadmill Training in Mice

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Published on: May 5, 2022

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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Published on: May 23, 2021

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System

Published on: April 11, 2025

Area of Science:

Biomedical Engineering
Signal Processing
Artificial Intelligence

Background:

Electrocardiogram (ECG) interpretation is often hindered by noise and disturbances.
Existing ECG signal quality indicators rely on human-defined properties, limiting their effectiveness.
A need exists for more robust and adaptable signal quality assessment methods.

Purpose of the Study:

To develop a data-driven quality indicator for identifying clean ECG signal segments.
To overcome the limitations of traditional, human-defined signal quality indicators.
To evaluate the performance of the proposed data-driven indicator against traditional methods.

Main Methods:

An unsupervised deep learning model, specifically an auto-encoder, was employed to derive the data-driven quality indicator.
The performance of the developed indicator was compared with traditional indicators across various quality assessment scenarios.
The methodology was designed to be adaptable to other data modalities.

Main Results:

The data-driven quality indicator demonstrated consistent and strong performance across different tasks.
Traditional signal quality indicators exhibited variable performance depending on the specific task.
The proposed method offers a more reliable approach to ECG signal quality assessment.

Conclusions:

Data-driven quality indicators show significant potential for enhancing ECG processing by eliminating reliance on expert-defined signal characteristics.
The developed auto-encoder based method provides a robust alternative for signal quality assessment.
The methodology's extensibility suggests broad applicability in analyzing other data types.