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Related Concept Videos

Equipments Used To Measure Blood Pressure01:30

Equipments Used To Measure Blood Pressure

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This invasive approach involves cannulating a peripheral artery. During each cardiac contraction, pressure generates mechanical motion within the catheter, transmitted through rigid, fluid-filled tubing to a transducer. This transducer converts mechanical motion into electrical signals displayed as waveforms on a monitor. An automatic flushing system prevents blood backflow. Due to the potential risk of unexpected arterial blood loss, this method is primarily used in intensive...
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Blood pressure measurement is a fundamental clinical procedure, providing crucial data for assessing cardiovascular health. Among the various sites for this measurement, the brachial and popliteal arteries are predominantly utilized due to their accessibility and the reliability of their readings. This lesson delves into the anatomical significance, methodology, and considerations of measuring blood pressure at these locations.
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Assessment of blood pressure in brachial artery(two-step method)01:23

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Measuring blood pressure is a fundamental skill in healthcare that aids in diagnosing and monitoring hypertension and other cardiovascular conditions. An aneroid sphygmomanometer, commonly used in clinical settings, offers a manual and precise method for blood pressure measurement. The technique for using this instrument involves specific steps that must be carefully executed to ensure accuracy. The following detailed description outlines a two-step technique for assessing blood pressure using...
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Assessment of blood pressure in brachial artery(one-step method)01:15

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This procedural guide systematically measures blood pressure using an oscillometric digital sphygmomanometer, emphasizing accuracy, patient safety, and comfort.
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Related Experiment Video

Updated: Aug 29, 2025

Establishment and Validation of a Rat Model of Pulmonary Arterial Hypertension Associated with Pulmonary Fibrosis
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An artificial intelligence-based noninvasive solution to estimate pulmonary artery pressure.

Jianwei Zheng1, Islam Abudayyeh2, Georgi Mladenov2

  • 1Schmid College of Science and Technology, Chapman University, Orange, CA, United States.

Frontiers in Cardiovascular Medicine
|September 12, 2022
PubMed
Summary

An artificial intelligence (AI) algorithm accurately predicts pulmonary artery pressure (PAP) waveforms using non-invasive signals. This AI model, combined with data preprocessing, offers precise PAP waveform prediction from readily available patient data.

Keywords:
artificial intelligencecomputational modeling methodsheart failurenoninvasivepulmonary artery pressure

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Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Cardiovascular Physiology

Background:

  • Pulmonary artery pressure (PAP) is a critical hemodynamic parameter.
  • Accurate PAP monitoring is essential for managing various cardiovascular conditions.
  • Current methods for PAP measurement can be invasive and carry risks.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) algorithm for predicting pulmonary artery pressure (PAP) waveforms.
  • To utilize non-invasive signal inputs for accurate PAP waveform estimation.
  • To explore the efficacy of combining signal preprocessing with AI for hemodynamic monitoring.

Main Methods:

  • A Residual Convolutional Network (RCN) was trained on a dataset of 180 patients with pulmonary artery catheter (PAC) data.
  • Waveform data included PAP, artery blood pressure (ABP), central venous pressure (CVP), respiration (RESP), photoplethysmogram (PPG), and electrocardiogram (ECG).
  • Wavelet scattering transform was employed for data preprocessing, and the model was validated on separate datasets.

Main Results:

  • The AI algorithm achieved a high coefficient of determination (R²) of 90.78% in predicting PAP waveforms.
  • Performance metrics included a mean square error of 11.55 and mean absolute error of 2.42.
  • The model demonstrated strong predictive accuracy, with an explained variance score of 90.87%.

Conclusions:

  • The developed AI approach precisely predicts PAP waveforms using non-invasive signals.
  • Combining data preprocessing, sampling methods, and AI algorithms enhances predictive accuracy.
  • This non-invasive method holds potential for improved hemodynamic monitoring and patient management.