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Related Concept Videos

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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Electrocardiogram01:29

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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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Related Experiment Video

Updated: Mar 8, 2026

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
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A Hidden Markov Model for Seismocardiography.

Johan Wahlstrom, Isaac Skog, Peter Handel

    IEEE Transactions on Bio-Medical Engineering
    |January 17, 2017
    PubMed
    Summary
    This summary is machine-generated.

    A new hidden Markov model accurately processes seismocardiograms for heart rate and variability estimation. This method improves upon existing techniques, offering precise cardiac time interval measurements for at-home monitoring.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Cardiovascular Physiology

    Background:

    • Seismocardiography (SCG) offers a non-invasive method for monitoring cardiac activity using inertial sensors.
    • Accurate extraction of cardiac information from SCG signals remains challenging due to signal complexity and noise.
    • Existing methods often struggle with beat-to-beat variations and sensor noise, limiting their clinical utility.

    Observation:

    • A novel hidden Markov model (HMM) approach is proposed for SCG signal processing.
    • The Expectation-Maximization (EM) algorithm learns SCG morphology, while the Viterbi algorithm estimates cardiac states.
    • This framework explicitly models sensor noise and morphological variations.

    Findings:

    • The HMM approach significantly outperforms state-of-the-art methods in estimating heart rate and heart rate variability from SCG.
    • Accurate estimation of isovolumic contraction time (IVCT) and left ventricular ejection time (LVET) with low mean absolute errors (approx. 5 ms and [Formula: see text], respectively).
    • The algorithm demonstrates robustness, requiring no assumptions on SCG morphology and adaptable to various inertial sensors.

    Implications:

    • Enables low-cost, accurate cardiovascular monitoring using readily available inertial sensors.
    • Facilitates remote patient monitoring and at-home healthcare services for cardiac assessment.
    • Provides a robust framework for analyzing complex SCG signals, advancing non-invasive cardiac diagnostics.