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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

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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.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Instrumentation Amplifier01:25

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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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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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Disturbances in Heart Rhythm01:29

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Arrhythmia or dysrhythmia refers to an abnormal heart rhythm caused by a defect in the heart's conduction system. It can cause the heart to beat irregularly, too quickly, or too slowly, leading to symptoms like chest pain, shortness of breath, and fainting. Factors such as stress, caffeine, alcohol, nicotine, cocaine, certain drugs, congenital defects, diseases, and electrolyte abnormalities can trigger arrhythmias.
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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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Special considerations while measuring pulse01:13

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Assessing a patient's pulse is a fundamental skill in healthcare, but certain situations require special attention:
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Related Experiment Video

Updated: Mar 27, 2026

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
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A robust heartbeat detector not depending on ECG sampling rate.

Piotr Augustyniak

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a robust regression-based QRS detector for reliable electrocardiogram (ECG) interpretation. The novel algorithm accurately identifies heartbeats even with noisy signals and missing data, improving diagnostic quality.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Cardiology

    Background:

    • Reliable heart beat detection is fundamental for accurate electrocardiogram (ECG) interpretation.
    • Existing algorithms can be sensitive to noise and signal artifacts, impacting diagnostic quality.

    Purpose of the Study:

    • To develop a robust regression-based QRS detector for improved heart beat detection in ECG signals.
    • To create an algorithm resilient to outliers and high-frequency noise without signal filtering.

    Main Methods:

    • The algorithm employs sliding windows of two lengths to identify best-fitted segments.
    • A running angle function between segments is calculated to derive heartbeat occurrence probability.
    • The method is independent of signal filtering and sampling frequency, accommodating missing data.

    Main Results:

    • The regression-based QRS detector demonstrates robustness against singular outliers and high-frequency noise.
    • The algorithm effectively determines heartbeat occurrence probability.
    • Performance is consistent across various sampling frequencies and tolerates missing data.

    Conclusions:

    • The proposed QRS detector enhances the reliability of heart beat detection in ECG analysis.
    • This method offers a versatile and robust solution for real-world ECG signal processing.
    • The algorithm's independence from sampling intervals and filtering makes it broadly applicable.