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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...

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...

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...

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Related Experiment Video

Updated: Mar 7, 2026

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

Toward Improving Electrocardiogram (ECG) Biometric Verification using Mobile Sensors: A Two-Stage Classifier

Robin Tan¹, Marek Perkowski²

¹Department of Electrical and Computer Engineering, Portland State University, Portland, OR 97201, USA. rtan@pdx.edu.

Sensors (Basel, Switzerland)

|February 24, 2017

Summary

Mobile electrocardiogram (ECG) biometrics offer enhanced security for remote access. A novel two-stage classifier using random forest and wavelet distance achieved 99.52% accuracy in identity recognition.

Keywords:

biometric recognition data security electrocardiogram (ECG)random forest wavelet distance measure

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Related Experiment Videos

Last Updated: Mar 7, 2026

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

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

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:

Biometrics and Signal Processing
Cybersecurity and Data Security
Mobile Health Technology

Background:

Electrocardiogram (ECG) signals from mobile devices present opportunities for biometric identity recognition.
Enhanced data security is crucial for remote access control systems.
Current biometric systems may lack robustness in diverse conditions.

Purpose of the Study:

To develop and evaluate a novel two-stage classifier for robust ECG-based biometric identification using mobile devices.
To improve the effectiveness and accuracy of biometric recognition systems leveraging mobile ECG data.
To assess the algorithm's performance across a diverse dataset including various health conditions.

Main Methods:

A two-stage classification algorithm combining Random Forest and Wavelet Distance Measure was developed.
A probabilistic threshold schema was integrated to optimize classification decisions.
The algorithm was validated on a mixed dataset comprising 184 subjects.

Main Results:

The proposed two-stage classifier achieved a subject verification accuracy of 99.52%.
This performance surpasses the accuracy of Random Forest alone (98.33%) and Wavelet Distance Measure alone (96.31%).
The algorithm demonstrated superior effectiveness and robustness in biometric identification.

Conclusions:

The novel two-stage classifier significantly enhances ECG-based biometric identification accuracy and reliability.
The proposed method is practical for applications requiring high data security, such as cloud security, cybersecurity, and remote healthcare.
Mobile ECG biometrics offer a promising solution for secure remote authentication.