IEEE J Biomed Health Inform. 2016 Mar;20(2):440-9. doi: 10.1109/JBHI.2015.2403283. Epub 2015 Feb 12.
Over past few years our group has been working on the development of a low-cost device, ARTSENS, for measurement of local arterial stiffness (AS) of the common carotid artery (CCA). This uses a single element ultrasound transducer to obtain A-mode frames from the CCA. It is designed to be fully automatic in its operation such that, a general medical practitioner can use the device without any prior knowledge of ultrasound modality. Placement of the probe over CCA and identification of echo positions corresponding to its two walls are critical steps in the process of measurement of AS. We had reported an algorithm to locate the CCA walls based on their characteristic motion. Unfortunately, in supine position, the internal jugular vein (IJV) expands in the carotid triangle and pulsates in a manner that confounds the existing algorithm and leads to wrong measurements of the AS. Jugular venous pulse (JVP), on its own right, is a very important physiological signal for diagnosis of morbidities of the right side of the heart and there is a lack of noninvasive methods for its accurate estimation. We integrated an ECG device to the existing hardware of ARTSENS and developed a method based on physiology of the vessels, which now enable us to segregate the CCA pulse (CCP) and the JVP. False identification rate is less than 4%. To retain the capabilities of ARTSENS to operate without ECG, we designed another method where the classification can be achieved without an ECG, albeit errors are a bit higher. These improvements enable ARTSENS to perform automatic measurement of AS even in the supine position and make it a unique and handy tool to perform JVP analysis.
在过去的几年中,我们的团队一直在致力于开发一种低成本的设备,即 ARTSENS,用于测量颈总动脉(CCA)的局部动脉僵硬度(AS)。该设备使用单个超声换能器从 CCA 获得 A 型帧。它的设计完全自动化,即使是没有超声模式知识的普通医生也可以使用该设备。将探头放置在 CCA 上,并识别对应于其两个壁的回波位置是测量 AS 的过程中的关键步骤。我们已经报道了一种基于其特征运动来定位 CCA 壁的算法。不幸的是,在仰卧位时,颈内静脉(IJV)在颈动脉三角扩张并以混淆现有算法的方式脉动,导致 AS 的错误测量。颈静脉搏动(JVP)本身就是诊断右侧心脏疾病的非常重要的生理信号,目前缺乏其准确估计的非侵入性方法。我们将心电图设备集成到 ARTSENS 的现有硬件中,并开发了一种基于血管生理学的方法,现在可以使我们将 CCA 脉冲(CCP)和 JVP 分开。错误识别率小于 4%。为了保留 ARTSENS 在没有 ECG 的情况下运行的功能,我们设计了另一种方法,即使没有 ECG 也可以实现分类,尽管误差略高。这些改进使 ARTSENS 即使在仰卧位也能自动测量 AS,并使其成为进行 JVP 分析的独特且方便的工具。