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Related Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

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The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
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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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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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Measurement of Blood Pressure01:17

Measurement of Blood Pressure

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Assessing blood pressure is a standard procedure executed in virtually all medical environments. The method utilized today was established over a hundred years ago by an innovative Russian doctor, Dr. Nikolai Korotkoff. The soft ticking noise, known as Korotkoff sounds, heard while taking blood pressure readings results from turbulent blood flow within the vessels. The apparatus required for this procedure includes a sphygmomanometer, a blood pressure cuff attached to a gauge, and a...
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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.
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Pulse Oximetry01:24

Pulse Oximetry

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Pulse oximetry, or SpO2, is a non-invasive method for continuously monitoring arterial oxygen saturation (SaO2). This procedure involves attaching a probe or sensor to the patient's fingertip, forehead, earlobe, or nose bridge. The sensor works by detecting changes in oxygen saturation levels through light signals generated by the oximeter and reflected by the pulsing blood under the probe.
Purpose
Average SpO2 values are greater than 95%. If the readings fall below 90%, it indicates that...
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Related Experiment Video

Updated: Oct 26, 2025

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

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HRV-Spark: Computing Heart Rate Variability Measures Using Apache Spark.

Xufeng Qu1, Yuanyuan Wu1, Jinze Liu1

  • 1Department of Computer Science, University of Kentucky, Lexington, Kentucky, USA.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|August 2, 2021
PubMed
Summary
This summary is machine-generated.

Heart rate variability (HRV) analysis using ECG signals is crucial. HRV-Spark, a cloud computing approach, efficiently computes HRV measures in parallel, demonstrating scalability for large datasets.

Keywords:
Amazon Web ServicesApache SparkCloud ComputingHeart Rate Variability

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Area of Science:

  • Biomedical Engineering
  • Computational Biology
  • Health Informatics

Background:

  • Heart rate variability (HRV) analysis is a key clinical research marker.
  • Increasing volumes of heart rate data from devices like ECG require efficient processing.
  • Existing methods for HRV analysis from ECG signals need improvement for large-scale applications.

Purpose of the Study:

  • To introduce HRV-Spark, a novel cloud computing approach for parallel HRV analysis.
  • To leverage Apache Spark and a QRS detection algorithm for efficient HRV computation.
  • To evaluate the performance and scalability of HRV-Spark on large datasets.

Main Methods:

  • Developed HRV-Spark using Apache Spark for parallel processing.
  • Integrated a QRS detection algorithm for ECG signal analysis.
  • Deployed and tested HRV-Spark on Amazon Web Services (AWS) clusters with large datasets from the National Sleep Research Resource.

Main Results:

  • HRV-Spark demonstrated efficient parallel computation of HRV measures.
  • Performance and scalability were validated across varying numbers of AWS computing nodes and dataset sizes.
  • The approach proved effective regardless of the hardware configuration of computing nodes.

Conclusions:

  • HRV-Spark offers an efficient and scalable solution for computing HRV measures from ECG signals.
  • The cloud-based approach addresses the need for timely processing of large-scale heart rate data.
  • This method facilitates advanced HRV analysis in clinical research and healthcare.