Zuo Wangmeng, Wang Peng, Zhang David
IEEE J Biomed Health Inform. 2016 Jan;20(1):119-27. doi: 10.1109/JBHI.2014.2369821. Epub 2014 Dec 10.
Increasing interest has been focused on computational pulse diagnosis where sensors are developed to acquire pulse signals, and machine learning techniques are exploited to analyze health conditions based on the acquired pulse signals. By far, a number of sensors have been employed for pulse signal acquisition, which can be grouped into three major categories, i.e., pressure, photoelectric, and ultrasonic sensors. To guide the sensor selection for computational pulse diagnosis, in this paper, we analyze the physical meanings and sensitivities of signals acquired by these three types of sensors. The dependence and complementarity of the different sensors are discussed from both the perspective of cardiovascular fluid dynamics and comparative experiments by evaluating disease classification performance. Experimental results indicate that each sensor is more appropriate for the diagnosis of some specific disease that the changes of physiological factors can be effectively reflected by the sensor, e.g., ultrasonic sensor for diabetes and pressure sensor for arteriosclerosis, and improved diagnosis performance can be obtained by combining three types of signals.
越来越多的关注集中在计算脉搏诊断上，即开发传感器来获取脉搏信号，并利用机器学习技术根据获取的脉搏信号分析健康状况。到目前为止，已经有多种传感器用于脉搏信号采集，可分为三大类，即压力传感器、光电传感器和超声传感器。为了指导计算脉搏诊断中的传感器选择，本文分析了这三种类型传感器所采集信号的物理意义和灵敏度。从心血管流体动力学和比较实验的角度，通过评估疾病分类性能，讨论了不同传感器之间的依赖性和互补性。实验结果表明，每种传感器更适合诊断某些特定疾病，这些疾病的生理因素变化能被该传感器有效反映，例如超声传感器用于诊断糖尿病，压力传感器用于诊断动脉硬化，并且通过组合三种类型的信号可以获得更高的诊断性能。
