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Related Concept Videos

Electrocardiogram01:29

Electrocardiogram

4.8K
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...
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Electrocardiogram Fundamentals01:28

Electrocardiogram Fundamentals

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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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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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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....
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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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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...
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Related Experiment Video

Updated: Dec 2, 2025

Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice

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An Efficient ECG Classification System Using Resource-Saving Architecture and Random Forest.

Bo-Han Kung, Po-Yuan Hu, Chiu-Chang Huang

    IEEE Journal of Biomedical and Health Informatics
    |November 2, 2020
    PubMed
    Summary

    This study introduces an efficient system for electrocardiogram (ECG) analysis, significantly reducing data size for arrhythmia classification. The method achieves high accuracy in detecting supraventricular ectopic beats (SVEB) and ventricular ectopic beats (VEB).

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Cardiology

    Background:

    • Electrocardiogram (ECG) signal analysis is crucial for diagnosing heart conditions.
    • Existing methods for ECG feature extraction and arrhythmia classification can be resource-intensive.
    • There is a need for efficient and accurate systems, especially for wearable and implantable devices.

    Purpose of the Study:

    • To develop a resource-saving system for extracting key ECG signal features.
    • To implement real-time classifiers for identifying different types of arrhythmias.
    • To evaluate the system's performance and applicability in practical healthcare settings.

    Main Methods:

    • Utilized two delta-sigma modulators with a 250 Hz sampling rate and three wave detection algorithms for feature extraction.
    • Encoded essential heartbeat details into a compact 68-bit data format.
    • Employed random forest classifiers trained with a patient-specific protocol using the MIT-BIH database and AAMI standards.

    Main Results:

    • Achieved a data compression of 98.52% compared to other methods.
    • Classified supraventricular ectopic beats (SVEB) with an F1 score of 81.05%.
    • Classified ventricular ectopic beats (VEB) with an F1 score of 97.07%, comparable to state-of-the-art techniques.

    Conclusions:

    • The proposed system offers a reliable, accurate, time-efficient, and low-complexity approach for ECG analysis.
    • The method's low memory usage makes it suitable for wearable and implantable medical devices.
    • Enables practical applications in wave detection and real-time arrhythmia classification.