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相关概念视频

Assessing Blood pressure using a doppler ultrasound01:19

Assessing Blood pressure using a doppler ultrasound

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:
Preparation of Equipment:
Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation01:21

Peripheral Arterial Disease II: Clinical Manifestations and Diagnostic Evaluation

Clinical manifestationsPeripheral Arterial Disease (PAD) manifests through a range of symptoms, from the characteristic intermittent claudication to atypical presentations and severe complications in advanced stages. Intermittent claudication, a hallmark symptom of PAD, presents as exercise-induced muscle pain that typically resolves within minutes of rest. This pain is reproducible and stems from inadequate blood flow, leading to the accumulation of lactic acid produced during anaerobic...

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相关实验视频

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A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology
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基于超声波数据的基于神经网络的动脉直径估计.

Zhuangzhuang Yu1,2, Manolis Sifalakis1, Borbála Hunyadi2

  • 1Department of Signal Processing & Modelling, imec The Netherlands / Holst Centre, Eindhoven, The Netherlands.

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概括

机器学习模型通过超声波准确地跟踪动脉直径,有助于预防心血管疾病. 这种自动化方法最大限度地减少了错误,并且适用于便携式监控.

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 心血管疾病是导致死亡的主要原因.
  • 通过超声波早期发现动脉异常对于预防至关重要.
  • 自动带直径波形提取对于血液动力学分析至关重要,但由于生理变异性而具有挑战性.

研究的目的:

  • 开发数据驱动的机器学习 (ML) 模型,从超声波数据中自动提取动脉直径.
  • 在嵌入式系统中部署最小化计算复杂性.
  • 在不需要临床医生的注释或手工制作的启发式信息的情况下,跟踪动脉直径.

主要方法:

  • 使用两个卷积神经网络 (NN) 模型和平滑波器开发了一个ML管道.
  • 第一个NN检测感兴趣的区域 (ROI),第二个NN追踪动脉直径.
  • 超声波信号以500Hz的频率获取,并使用基于数字信号处理 (DSP) 的方法作为参考来训练ML管道.

主要成果:

  • ML管道与DSP参考波形实现了近乎完美的时间对齐 (皮尔森相关系数r = 0.87).
  • 动脉直径预测的平均绝对偏差为0.077毫米 (1%的误差).
  • 基于NN的方法证明了对漂移和工件的稳定性.

结论:

  • 拟议的ML管道提供了一种全自动,准确和计算效率高的方法来跟踪动脉直径.
  • 这种方法消除了对专家干预和手工微调的需求,与当前的临床实践和传统的DSP方法不同.
  • ML管道在小数据集上的可训练性和适合A模式超声波的适用性使其成为小型化和在线临床/健行监测的前景.