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Electrocardiogram01:29

Electrocardiogram

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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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Electrocardiogram Fundamentals01:28

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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.
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Correlation between ECG and Cardiac Cycle01:25

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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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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
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Assessing Blood pressure using a doppler ultrasound01:19

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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
Preparation of Equipment:
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Ejection Wave Segmentation for Contact-Free Heart Rate Estimation from Ballistocardiographic Signals.

Samuel M Proll, Stefan Hofbauer, Christian Kolbitsch

    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

    We developed a novel algorithm for detecting peaks in ballistocardiographic (BCG) signals to accurately estimate heart rate. This method achieves performance comparable to state-of-the-art techniques, even with low-quality clinical data.

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    Area of Science:

    • Biomedical Engineering
    • Cardiovascular Physiology
    • Signal Processing

    Background:

    • Ballistocardiography (BCG) offers a non-invasive method for monitoring cardiovascular function.
    • Accurate heart rate estimation from BCG signals is challenging due to signal noise and variability.
    • Existing peak detection algorithms for BCG require further refinement for clinical applications.

    Purpose of the Study:

    • To introduce a new algorithm for enhanced peak detection in ballistocardiographic (BCG) signals.
    • To utilize the developed algorithm for precise heart rate estimation.
    • To evaluate the algorithm's performance against existing methods using clinical data.

    Main Methods:

    • A novel algorithm for BCG peak detection using local maxima and weighted summation of peak heights.
    • Enhancement of systolic complexes and estimation of coarse heart beat locations.
    • Simultaneous detection of ejection waves (I, J, K) around estimated beat locations.
    • Performance assessment via heart rate estimation due to lack of reference BCG annotations.
    • Evaluation on a low-quality BCG dataset from 42 patients in a clinical setting.

    Main Results:

    • The algorithm achieved a mean absolute percentage error of 2.58% at 65% coverage for heart rate estimation.
    • Performance is comparable to the best state-of-the-art algorithms investigated.
    • Limits of agreement with ECG-based heart rate measurements were within -3.63 and 5.78 beat/min (5th/95th percentiles).

    Conclusions:

    • The presented peak detection algorithm provides accurate heart rate estimation from BCG signals.
    • The method demonstrates robustness and effectiveness even with low-quality, realistic clinical data.
    • This algorithm represents a significant advancement for non-invasive cardiovascular monitoring using BCG.