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Related Concept Videos

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.
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Average SpO2 values are greater than 95%. If the readings fall below 90%, it indicates that...
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Assessing Blood pressure using a doppler ultrasound01:19

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To obtain accurate blood pressure measurements in clinical settings, especially when traditional methods are insufficient, healthcare professionals utilize the Doppler ultrasound technique. This method uses high-frequency sound waves to detect blood flow within the arteries, which is crucial for patients with conditions that complicate circulatory system assessment.
Pre-Procedural Guidelines for Doppler Ultrasound Blood Pressure Assessment:
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Sites for measruring blood pressure01:21

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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(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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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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Electrocardiogram01:29

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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Related Experiment Video

Updated: Sep 25, 2025

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
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pyVHR: a Python framework for remote photoplethysmography.

Giuseppe Boccignone1, Donatello Conte2, Vittorio Cuculo1

  • 1PHuSe Lab - Dipartimento di Informatica, Università degli Studi di Milano, Milan, Italy.

Peerj. Computer Science
|May 2, 2022
PubMed
Summary
This summary is machine-generated.

The pyVHR framework enables accurate heart rate (HR) variability estimation from videos using remote photoplethysmography (rPPG). This tool supports developing, assessing, and comparing HR monitoring methods without wearable sensors, achieving real-time processing speeds.

Keywords:
Contactless monitoringDeep rPPGDeepfake DetectionHeart Rate EstimationRemote photoplethysmography

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Computer Vision

Background:

  • Remote photoplethysmography (rPPG) aims to estimate heart rate (HR) variability from videos in real-world settings.
  • Various data-driven, model-based, and statistical methods have been developed over the past two decades for BVP signal estimation.
  • Recent advancements include learning-based rPPG methods, enhancing the accuracy of HR monitoring.

Purpose of the Study:

  • Introduce the pyVHR framework for comprehensive HR fluctuation analysis.
  • Provide a platform for developing, assessing, and comparing traditional and learning-based rPPG methods.
  • Facilitate theoretical studies and practical applications where wearable sensors are not feasible.

Main Methods:

  • A multi-stage pipeline for extracting and analyzing HR fluctuations using rPPG.
  • Leverages accelerated Python libraries for efficient video and signal processing.
  • Incorporates parallel/accelerated procedures for potential online processing on a GPU.

Main Results:

  • The pyVHR framework supports the development and assessment of novel rPPG methods.
  • Enables sound comparison of established rPPG methods across multiple datasets.
  • Achieves real-time processing for 30 fps HD videos with an average speedup of approximately 5x.

Conclusions:

  • pyVHR offers a versatile framework for advancing rPPG research and applications.
  • The framework is optimized for performance, enabling real-time HR variability analysis.
  • It serves as a valuable tool for both researchers and practitioners in the field of non-contact HR monitoring.