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Related Concept Videos

Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

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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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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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Measurement of Blood Pressure01:17

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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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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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Neural Regulation of Blood Pressure01:18

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The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
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Pre-Procedural Guidelines for Assessing Blood Pressure01:10

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Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
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Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom
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Central Aortic Blood Pressure Waveform Estimation with a Temporal Convolutional Network.

Wenyan Liu, Shuo Du, Na Pang

    IEEE Journal of Biomedical and Health Informatics
    |April 20, 2023
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    Summary
    This summary is machine-generated.

    A new temporal convolutional network (TCN) model accurately reconstructs central aortic blood pressure waveforms from radial measurements. This AI approach surpasses traditional methods in speed and precision, aiding cardiovascular disease monitoring.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Medicine
    • Cardiovascular Physiology

    Background:

    • Accurate central aortic blood pressure (aBP) monitoring is crucial for cardiovascular health assessment.
    • Traditional methods for aBP waveform reconstruction often require manual feature extraction and can be computationally intensive.
    • Developing non-invasive, accurate, and efficient methods for aBP measurement is an ongoing challenge.

    Purpose of the Study:

    • To introduce and evaluate a novel temporal convolutional network (TCN) model for reconstructing central aortic blood pressure (aBP) waveforms.
    • To compare the TCN model's accuracy and computational efficiency against a convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) model.
    • To assess the TCN model's potential for early cardiovascular disease monitoring.

    Main Methods:

    • A temporal convolutional network (TCN) model was developed to derive central aBP waveforms from radial blood pressure waveforms.
    • The TCN model was trained and validated using a measured database (1,032 participants) and a public database (4,374 virtual subjects).
    • Performance was evaluated by comparing the TCN model against a CNN-BiLSTM model using root mean square error (RMSE) and computational time.

    Main Results:

    • The TCN model demonstrated superior accuracy, with RMSE values of 0.55 ± 0.40 mmHg (measured) and 0.84 ± 0.29 mmHg (public database).
    • The TCN model exhibited significantly lower computational costs, with training times of 9.63 min and 25.51 min, and average test times of 1.79 ms and 8.58 ms.
    • The TCN model outperformed the CNN-BiLSTM model in both accuracy and computational efficiency.

    Conclusions:

    • The novel TCN model provides an accurate and computationally efficient method for reconstructing central aortic blood pressure waveforms.
    • This AI-driven approach eliminates the need for manual feature extraction, simplifying the measurement process.
    • The TCN model's speed and accuracy offer a promising tool for non-invasive cardiovascular monitoring and potential early disease detection.