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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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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.
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Related Experiment Video

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Analyzing Long-Term Electrocardiography Recordings to Detect Arrhythmias in Mice
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A Robust Machine Learning Architecture for a Reliable ECG Rhythm Analysis during CPR.

Iraia Isasi, Unai Irusta, Andoni Elola

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
    Summary

    This study developed a machine learning (ML) framework to improve automated external defibrillator (AED) shock decisions during cardiopulmonary resuscitation (CPR). The ML approach accurately distinguished shockable rhythms despite CPR-induced ECG artifacts.

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    Area of Science:

    • Biomedical Engineering
    • Cardiology
    • Artificial Intelligence in Medicine

    Background:

    • Chest compressions during cardiopulmonary resuscitation (CPR) create electrocardiogram (ECG) artifacts, potentially compromising the accuracy of automated external defibrillator (AED) shock advice algorithms (SAA).
    • Existing methods combining adaptive filtering and machine learning (ML) show promise but require performance enhancement for reliable shock/no-shock decisions during CPR.

    Purpose of the Study:

    • To design and evaluate a robust ML framework for accurate shock/no-shock decision-making during CPR.
    • To improve the reliability of SAAs in the presence of CPR-induced ECG artifacts.

    Main Methods:

    • Utilized a dataset of 596 shockable and 1697 non-shockable ECG segments from out-of-hospital cardiac arrest cases.
    • Applied a Least Mean Squares (LMS) filter to remove CPR artifacts, followed by Stationary Wavelet Transform (SWT) for feature extraction.
    • Employed a wrapper-based feature selection method to identify the 6 most effective features for classification.
    • Tested four state-of-the-art ML classifiers for shock/no-shock determination.

    Main Results:

    • All tested ML classifiers achieved high performance metrics: Sensitivity (Se) > 94.5%, Specificity (Sp) > 95.5%, and accuracy around 96.0%.
    • The developed framework successfully exceeded the American Heart Association's recommended minimum performance standards (90% Se, 95% Sp).

    Conclusions:

    • The proposed robust ML framework demonstrates significant potential for reliable shock/no-shock decision-making during CPR.
    • This advancement could enhance AED performance in critical resuscitation scenarios by overcoming CPR-induced ECG artifact challenges.