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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.
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Introduction
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The Bode plot is an essential tool in control system analysis, mapping the frequency response of a system through a magnitude plot and a phase plot, both against a logarithmic frequency axis. To construct a Bode plot, consider the transfer function H(ω):
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Related Experiment Video

Updated: May 24, 2025

Real-Time Cardiac Mapping with a Noninvasive Imageless Electrocardiographic Imaging System
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ECG Signal Construction From Heart Sounds via Single Node, Surface Acoustic Sensing.

Kaylee Yaxuan Li, Yasha Iravantchi, Hyunmin Park

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
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    Summary

    This study transforms neck acoustic heart sounds into electrocardiogram (ECG) waveforms using a novel algorithm. This enables non-intrusive, daily cardiac rhythm monitoring with a single dry wearable device.

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    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Technology
    • Signal Processing

    Background:

    • Current electrocardiogram (ECG) monitoring is limited by intrusive hardware and stationary requirements.
    • Existing phonocardiogram methods lack precision for crucial cardiac rhythm analysis and are susceptible to noise.
    • There is a need for non-intrusive, mobile, and continuous cardiac rhythm monitoring solutions.

    Purpose of the Study:

    • To develop a method for generating ECG waveforms from acoustic heart sounds.
    • To enable unobtrusive, long-term, low-cost daily cardiac rhythm monitoring.
    • To validate the accuracy of acoustic ECG in capturing key cardiac metrics.

    Main Methods:

    • Utilized a wide bandwidth surface-acoustic-wave microphone placed on the neck to capture heart sounds via the carotid artery.
    • Employed a cross-modal autoencoder, an advanced algorithm, for transforming acoustic heart sound signals into ECG waveforms.
    • Conducted a 9-participant study to evaluate the generated ECG waveforms and metrics.

    Main Results:

    • Successfully constructed PQRST waveforms from acoustic heart sound signals.
    • Demonstrated accurate determination of critical PQRST metrics from the acoustic-derived ECG.
    • Showcased mobile acoustic ECG waveform construction during user ambulation (walking).

    Conclusions:

    • Acoustic heart sound to ECG transformation is feasible and effective.
    • This technology paves the way for unobtrusive, long-term, low-cost daily cardiac rhythm monitoring.
    • A single-node dry wearable device can provide valuable cardiac rhythm insights.