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Related Concept Videos

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.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
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Equipments Used To Measure Blood Pressure01:30

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Direct Method
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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Updated: Jun 22, 2025

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Cloud Computing to Enable Wearable-Driven Longitudinal Hemodynamic Maps.

Cyrus Tanade1, Emily Rakestraw1, William Ladd1

  • 1Duke University, Durham, NC, USA.

International Conference for High Performance Computing, Networking, Storage and Analysis : [Proceedings]. SC (Conference : Supercomputing)
|June 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a cloud-based framework for creating 3D patient-specific hemodynamic maps from wearable device data. This innovation enables long-term hemodynamic tracking, overcoming previous computational limitations.

Keywords:
cardiovascular diseasecloud computingcomputational fluid dynamicslongitudinal hemodynamic mapswearable sensors

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Area of Science:

  • Cardiovascular research
  • Computational fluid dynamics
  • Medical device technology

Background:

  • Longitudinal hemodynamic monitoring is crucial but faces significant technological and computational hurdles.
  • Current personalized digital twin models require extensive computational resources, limiting their application for daily patient monitoring.
  • Existing methods struggle to extend hemodynamic flow sampling over extended periods.

Purpose of the Study:

  • To develop a novel cloud-based, parallel-in-time framework for generating 3D patient-specific longitudinal hemodynamic maps.
  • To overcome the computational limitations of current digital twin simulations for hemodynamics.
  • To enable continuous hemodynamic tracking using data from wearable devices.

Main Methods:

  • Implementation of a cloud-based, parallel-in-time computational framework.
  • Integration of continuous data streams from wearable devices.
  • Validation using established ground truth data for 750 heartbeats.
  • Leveraging an initial set of fixed simulations to inform wearable data.

Main Results:

  • Successful generation of the first 3D patient-specific, longitudinal hemodynamic maps.
  • Demonstrated validity of the proposed method against ground truth data.
  • Significant reduction in computational time compared to traditional simulation methods.

Conclusions:

  • The proposed framework effectively addresses the challenge of long-term hemodynamic monitoring.
  • Wearable device integration combined with cloud computing offers a viable solution for personalized, longitudinal hemodynamic assessment.
  • This approach paves the way for advanced patient monitoring and treatment evaluation.