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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...

Correlation between ECG and Cardiac Cycle

Correlation between ECG and Cardiac Cycle

The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...

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...

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Related Experiment Video

Updated: Sep 28, 2025

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function

Published on: December 11, 2019

Sharing personal ECG time-series data privately.

Luca Bonomi¹, Zeyun Wu², Liyue Fan³

¹Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Journal of the American Medical Informatics Association : JAMIA

|April 5, 2022

Summary

This summary is machine-generated.

Emerging wearable devices collect unique health data, posing privacy risks. A new method protects individual time-series data, like electrocardiography (ECG), for secure analysis and research.

Keywords:

ECG data data privacy data sharing predictive analytics time-series

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Last Updated: Sep 28, 2025

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Published on: April 26, 2024

Area of Science:

Health informatics
Data privacy
Biomedical engineering

Background:

Wearable devices generate unique, fine-grained individual time-series health data.
Sharing this data advances medical research but presents significant privacy challenges.
Existing privacy models may not adequately protect highly unique, individual-level health information.

Purpose of the Study:

To evaluate a novel privacy model for sharing individual-level time-series health data.
To enable secure data sharing while maintaining usability for analytics.
To address the privacy risks associated with unique, fine-grained health data.

Main Methods:

Proposed a privacy-protecting method for sharing individual-level electrocardiography (ECG) time-series data.
Utilized dimensional reduction techniques and random sampling for provable privacy protection.
Evaluated the method on two real-world ECG datasets.

Main Results:

The privacy risk was significantly reduced after data sanitization.
Data usability was retained for clinical tasks such as predictive modeling and clustering.
The solution demonstrated strong privacy protection against an informed adversarial model.

Conclusions:

Individual-level time-series health data is highly unique and requires novel privacy solutions.
The proposed method offers strong privacy protections for ECG data.
The method successfully preserves data utility for essential clinical analytics.