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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

973
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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Special considerations while measuring pulse01:13

Special considerations while measuring pulse

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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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Pulse amplitude and quality01:17

Pulse amplitude and quality

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Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
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Pulse01:16

Pulse

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When the heart pumps blood out, arterial elastic fibers play a crucial role in sustaining a high-pressure gradient. They expand to accommodate the received blood and then recoil - a process known as the pulse that can be either manually palpated or electronically quantified. Despite a reduction in its effect with increased distance from the heart, elements of the pulse's systolic and diastolic components persist, observable even at the arteriole level.
The pulse serves as a clinical...
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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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Assessment of apical radial pulse01:25

Assessment of apical radial pulse

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Apical-Radial (A-R) Pulse Assessment
The A-R pulse assessment involves simultaneous evaluation of the apical and radial pulses. When the apical and radial pulse rates vary, this assessment helps identify a pulse deficit.
Pre-Procedural Preparation
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Related Experiment Video

Updated: Oct 5, 2025

Ultrasound-based Pulse Wave Velocity Evaluation in Mice
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A machine learning strategy for fast prediction of cardiac function based on peripheral pulse wave.

Sirui Wang1, Dandan Wu1, Gaoyang Li2

  • 1Graduate School of Engineering, Chiba University, Chiba, 263-8522, Japan.

Computer Methods and Programs in Biomedicine
|February 1, 2022
PubMed
Summary

Machine learning accurately predicts cardiovascular function using pulse wave data. This approach shows potential for non-invasive health monitoring and cardiovascular disease diagnosis.

Keywords:
Cardiovascular disease (CVD)Cardiovascular functionMachine learningPulse wave

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Area of Science:

  • Cardiovascular physiology
  • Biomedical engineering
  • Machine learning applications

Background:

  • Pulse waves serve as crucial indicators of cardiovascular system (CVS) health.
  • Predicting cardiovascular function parameters is vital for diagnosing cardiovascular diseases (CVDs).
  • Machine learning (ML) offers powerful feature-abstraction for pulse wave analysis, yet remains understudied for clinical applications.

Purpose of the Study:

  • To develop and validate an ML-based strategy for fast and accurate prediction of key cardiovascular function parameters.
  • To assess the model's performance in distinguishing between healthy subjects and those with CVDs.
  • To explore the clinical significance of pulse wave analysis for health monitoring and CVD diagnosis.

Main Methods:

  • An ML model utilizing a multi-layered, fully connected network was developed.
  • Two high-quality pulse wave datasets were curated: one healthy, one CVD-subject group (412 subjects total).
  • The model was optimized to predict arterial compliance (AC), total peripheral resistance (TPR), and stroke volume (SV).

Main Results:

  • The ML model demonstrated high accuracy in predicting TPR and SV for both healthy (85.3%, 86.9%) and CVD subjects (88.3%, 89.2%).
  • Model predictions showed strong consistency with clinical measurements.
  • Error analysis confirmed the model's predictive capabilities.

Conclusions:

  • The developed ML strategy shows feasibility for predicting physiological and pathological CVS conditions.
  • The subject groups accurately represent typical population characteristics.
  • Further research with larger datasets is needed for disease-specific predictions, such as for heart failure.