Kumar Ravinder, Kumar Vishal, Rich Collin, Lemmerhirt David, Fowlkes J Brian, Sahani Ashish Kumar
Department of Bioengineering, University of Pittsburgh, Swanson School of Engineering, 302 Benedum Hall 3700 O'Hara Street, Pittsburgh, PA, 15260, USA.
Department of Biomedical Engineering, Indian Institute of Technology, Ropar, Punjab, India.
Med Biol Eng Comput. 2025 May;63(5):1413-1426. doi: 10.1007/s11517-024-03268-9. Epub 2025 Jan 6.
Blood pressure (BP) is one of the vital physiological parameters, and its measurement is done routinely for almost all patients who visit hospitals. Cuffless BP measurement has been of great research interest over the last few years. In this paper, we aim to establish a method for cuffless measurement of BP using ultrasound. In this method, the arterial wall is pushed with an acoustic radiation force impulse (ARFI). After the completion of the ARFI pulse, the artery undergoes impulsive unloading which stimulates a hoop mode vibration. We designed two machine learning (ML) models which make it possible to estimate the internal pressure of the artery using ultrasonically measurable parameters. To generate the training data for the ML models, we did extensive finite element method (FEM) eigen frequency simulations for different tubes under pressure by sweeping through a range of values for inner lumen diameter (ILD), tube density (TD), elastic modulus, internal pressure (IP), tube length, and Poisson's ratio. Through image processing applied on images of different eigen modes supported for each simulated case, we identified its hoop mode frequency (HMF). Two different ML models were designed based on the simulated data. One is a four-parameter model (FPM) that takes tube thickness (TT), TD, ILD, and HMF as the inputs and gives out IP as output. Second is a three-parameter model (TPM) that takes TT, ILD, and HMF as inputs and IP as output. The accuracy of these models was assessed using simulated data, and their performance was confirmed through experimental verification on two arterial phantoms across a range of pressure values. The first prediction model (FPM) exhibited a mean absolute percentage error (MAPE) of 5.63% for the simulated data and 3.68% for the experimental data. The second prediction model (TPM) demonstrated a MAPE of 6.5% for simulated data and 8.73% for experimental data. We were able to create machine learning models that can measure pressure within an elastic tube through ultrasonically measurable parameters and verified their performance to be adequate for BP measurement applications. This work establishes a pathway for cuffless, continuous, real-time, and non-invasive measurement of BP using ultrasound.
血压（BP）是重要的生理参数之一，几乎所有到医院就诊的患者都会常规进行血压测量。在过去几年中，无袖带血压测量一直是研究的热点。在本文中，我们旨在建立一种使用超声进行无袖带血压测量的方法。在这种方法中，用声辐射力脉冲（ARFI）推动动脉壁。在ARFI脉冲完成后，动脉经历脉冲卸载，从而激发环向模式振动。我们设计了两个机器学习（ML）模型，使得利用超声可测量参数来估计动脉内压成为可能。为了生成ML模型的训练数据，我们通过扫描一系列内管腔直径（ILD）、管密度（TD）、弹性模量、内压（IP）、管长度和泊松比的值，对不同压力下的不同管道进行了广泛的有限元方法（FEM）本征频率模拟。通过对每个模拟案例所支持的不同本征模式图像进行图像处理，我们确定了其环向模式频率（HMF）。基于模拟数据设计了两种不同的ML模型。一种是四参数模型（FPM），它将管壁厚度（TT）、TD、ILD和HMF作为输入，并将IP作为输出。第二种是三参数模型（TPM），它将TT、ILD和HMF作为输入，并将IP作为输出。使用模拟数据评估了这些模型的准确性，并通过在两个动脉模型上跨越一系列压力值的实验验证确认了它们的性能。第一个预测模型（FPM）对模拟数据的平均绝对百分比误差（MAPE）为5.63%，对实验数据为3.68%。第二个预测模型（TPM）对模拟数据的MAPE为6.5%，对实验数据为8.73%。我们能够创建通过超声可测量参数来测量弹性管内压力的机器学习模型，并验证了它们的性能足以用于血压测量应用。这项工作建立了一条使用超声进行无袖带、连续、实时和无创血压测量的途径。
