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Beats01:09

Beats

The study of music provides many examples of the superposition of waves and the constructive and destructive interference that occurs. Very few examples of music being performed consist of a single source playing a single frequency for an extended period of time. A single frequency of sound for an extended period might be monotonous to the point of irritation, similar to the unwanted drone of an aircraft engine or a loud fan. Music is pleasant and exciting due to mixing the changing frequencies...
ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage. When...
Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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...
Dysrhythmias IV: Characteristics of Bradyarrhythmias01:18

Dysrhythmias IV: Characteristics of Bradyarrhythmias

Bradyarrhythmias are cardiac rhythm disorders characterized by a slower-than-normal heart rate, typically defined as fewer than 60 beats per minute. Some of which are discussed here:Sinus BradycardiaSinus bradycardia presents a heart rate lower than 60 beats per minute, with a regular rhythm originating from the SA node. The ECG typically shows normal P waves preceding each QRS complex, a normal PR interval (0.12 to 0.20 seconds), and a normal QRS duration (0.06 to 0.10 seconds).First-Degree AV...

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Related Experiment Video

Updated: Jul 10, 2026

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals
08:22

BrainBeats as an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

Published on: April 26, 2024

A robust method to estimate time split in second heart sound using instantaneous frequency analysis.

Isa Yildirim, Rashid Ansari

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 16, 2007
    PubMed
    Summary

    This study introduces an automated method to measure the aortic valve (A2) and pulmonary valve (P2) split, a crucial diagnostic indicator in heart sounds (S2). The novel approach uses instantaneous frequency analysis for accurate, noninvasive A2-P2 split measurement.

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    Published on: May 8, 2021

    Area of Science:

    • Cardiology
    • Biomedical Engineering
    • Signal Processing

    Background:

    • The second heart sound (S2) originates from the closure of aortic (A2) and pulmonary (P2) valves.
    • The time interval between A2 and P2, known as the A2-P2 split, holds significant diagnostic value.
    • Existing noninvasive methods for measuring A2-P2 split are limited by signal modeling assumptions or manual processing.

    Purpose of the Study:

    • To develop a novel, automated, and noninvasive method for measuring the A2-P2 split.
    • To minimize reliance on prior signal modeling assumptions in split measurement.

    Main Methods:

    • Utilizes the smoothed Wigner-Ville Distribution to obtain time-frequency representations of S2.
    • Tracks changes in the instantaneous frequency (IF) of S2 to identify the P2 pulse onset.
    • Employs an automated procedure for analyzing IF trajectory cues.

    Main Results:

    • Successfully demonstrated the procedure's effectiveness in estimating the A2-P2 split via simulations.
    • Investigated performance under varying noise levels (6-8 dB) and interference.
    • Showcased the robustness of the proposed automated method.

    Conclusions:

    • The proposed method offers an automated and noninvasive approach to measure the A2-P2 split.
    • This technique minimizes prior assumptions, enhancing its applicability in clinical settings.
    • The demonstrated robustness suggests potential for reliable diagnostic use.