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Related Concept Videos

Instrumentation Amplifier01:25

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...
Electrocardiogram01:29

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 Fundamentals01:28

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...
ECG Interpretation of Rhythms01:24

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...

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Related Experiment Video

Updated: Jun 14, 2026

Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
05:03

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

Published on: December 11, 2019

Building Intelligent ECG.

Leslie Mertz

    IEEE Pulse
    |June 12, 2026
    PubMed
    Summary
    This summary is machine-generated.

    HeartSciences Inc. developed new AI technology to analyze electrocardiogram (ECG) signals. This innovation detects early heart disease indicators by processing frequency and energy data for easier interpretation.

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    Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
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    Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

    Published on: December 5, 2025

    Related Experiment Videos

    Last Updated: Jun 14, 2026

    Patient Directed Recording of a Bipolar Three-Lead Electrocardiogram using a Smartwatch with ECG Function
    05:03

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

    Published on: December 11, 2019

    Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task
    07:08

    Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

    Published on: December 5, 2025

    Area of Science:

    • Cardiology and Biomedical Engineering
    • Artificial Intelligence in Healthcare
    • Signal Processing

    Background:

    • Standard electrocardiogram (ECG) interpretation has limitations in detecting subtle cardiac abnormalities.
    • Advancements in signal processing and AI offer potential for enhanced diagnostic capabilities.
    • Early detection of heart disease is crucial for improving patient outcomes.

    Purpose of the Study:

    • To introduce a novel technology for analyzing ECG signals.
    • To leverage artificial intelligence (AI) for extracting deeper insights from ECG data.
    • To facilitate the early identification of potential heart conditions.

    Main Methods:

    • Utilizing advanced signal processing techniques on ECG data.
    • Employing a cloud-based platform powered by AI algorithms.
    • Developing specialized hardware (MyoVista wavECG device) and software (MyoVista Insights platform).

    Main Results:

    • The technology successfully gathers frequency and energy data from ECG signals.
    • It retrieves diagnostic information not typically visible in standard ECG readings.
    • The system presents findings in an easily understandable format for clinicians.

    Conclusions:

    • The new AI-driven ECG analysis technology shows promise for early heart disease detection.
    • This approach enhances the information obtainable from ECG signals.
    • The MyoVista platform and device offer a novel tool for cardiological assessment.