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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

1.5K
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...
1.5K
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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Regulation of Pulse01:20

Regulation of Pulse

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Pulse regulation involves physiological mechanisms that ensure adequate blood flow throughout the body. The heartbeat, regulated by the autonomic nervous system, is influenced by hormonal balance, physical activity, and emotional state.
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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 oxygen saturation01:19

Special considerations while measuring oxygen saturation

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Assessing respiratory rate concurrently with pulse measurement is fundamental to patient care, providing valuable insights into the patient's respiratory function. The normal breathing rate for an adult usually falls within a normal range of 12 to 20 breaths per minute. Abnormal respiratory rates can signal underlying health conditions or the need for immediate intervention.
Ensuring accuracy in vital sign recordings while prioritizing patient comfort and minimizing anxiety is...
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Pulse01:16

Pulse

2.3K
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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A Real-Time Analysis Method for Pulse Rate Variability Based on Improved Basic Scale Entropy.

Yongxin Chou1, Ruilei Zhang1, Yufeng Feng2

  • 1School of Electrical and Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China.

Journal of Healthcare Engineering
|October 26, 2017
PubMed
Summary
This summary is machine-generated.

A new method, sliding window iterative base scale entropy analysis (SWIBSEA), efficiently analyzes pulse rate variability (PRV). SWIBSEA offers a faster, less memory-intensive alternative to base scale entropy analysis (BSEA) for real-time PRV monitoring.

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Area of Science:

  • Biomedical Engineering
  • Nonlinear Dynamics
  • Physiological Signal Analysis

Background:

  • Base scale entropy analysis (BSEA) is a nonlinear method for heart rate variability (HRV) but is time-consuming.
  • The applicability of BSEA to pulse rate variability (PRV) signals and its efficiency are not well-established.
  • Efficient analysis of PRV is crucial for real-time physiological monitoring.

Purpose of the Study:

  • To develop an efficient method for analyzing PRV signals.
  • To evaluate the suitability of BSEA for PRV analysis.
  • To introduce a novel method, sliding window iterative base scale entropy analysis (SWIBSEA), for enhanced PRV analysis.

Main Methods:

  • Developed SWIBSEA by integrating BSEA with sliding window iterative theory.
  • Utilized PRV signals derived from blood pressure data of healthy young and old subjects from the MIT/PhysioNet/Fantasia database.
  • Compared the performance of SWIBSEA against BSEA in terms of time consumption, memory usage, and entropy calculation.

Main Results:

  • SWIBSEA significantly reduced time consumption and buffer cache space compared to BSEA.
  • SWIBSEA achieved comparable entropy values to BSEA.
  • Observed similar trends in base scale entropy (BSE) changes for young and old subjects in PRV signals as previously reported for HRV signals.

Conclusions:

  • SWIBSEA is an efficient and effective method for analyzing PRV signals.
  • The method enables real-time derivation of information from both long-term and short-term PRV signals.
  • SWIBSEA holds potential for dynamic PRV analysis in portable and wearable medical devices.