Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan.
Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan; Institute of Medical Science and Technology, National Sun Yat-sen University, KaoHsiung 80424, Taiwan.
Comput Biol Med. 2015 Feb;57:26-31. doi: 10.1016/j.compbiomed.2014.10.016. Epub 2014 Oct 27.
To carry out a pulse diagnosis, a traditional Chinese medicine (TCM) physician presses the patient's wrist artery at three incremental depths, namely Fu (superficial), Zhong (medium), and Chen (deep). However, the definitions of the three depths are insufficiently clear for use with modern pulse diagnosis instruments (PDIs). In this paper, a quantitative method is proposed to express the pulse-taking depths based on the width of the artery (WA). Furthermore, an index, α, is developed for estimating WA for PDI application. The α value is obtained using an artificial neural network (ANN) model with contact pressure (CP) and sensor displacement (SD) as the inputs. The WA and SD data from an ultrasound instrument and CP and SD data from a PDI were analyzed. The results show that the mean prediction error and the standard deviation (STD) of the ANN model was 1.19% and 0.0467, respectively. Comparing the ANN model with the SD model by statistical method, it showed significant difference and the improvement in the mean prediction error and the STD was 71.62% and 29.78%, respectively. The α value can thus map WA with less individual variation than that of the values estimated directly using the SD model. Pulse signals at different depths thus can be acquired according to α value while using a PDI, providing TCM physicians with more reliable pulse information.
进行脉象诊断时，中医师在三个递增深度按压患者的腕动脉，即浮（浅）、中（中）和沉（深）。然而，这三个深度的定义对于现代脉象诊断仪器（PDI）的使用来说不够清晰。在本文中，提出了一种基于动脉宽度（WA）的定量方法来表示切脉深度。此外，还开发了一个指数α，用于估计 PDI 应用中的 WA。α 值是使用人工神经网络（ANN）模型，以接触压力（CP）和传感器位移（SD）作为输入来获得的。分析了来自超声仪器的 WA 和 SD 数据以及来自 PDI 的 CP 和 SD 数据。结果表明，ANN 模型的平均预测误差和标准差（STD）分别为 1.19%和 0.0467。通过统计方法比较 ANN 模型和 SD 模型，结果显示存在显著差异，平均预测误差和 STD 的改善分别为 71.62%和 29.78%。因此，与使用 SD 模型直接估计值相比，α 值可以更好地映射 WA，个体差异较小。这样，在使用 PDI 时可以根据α值获取不同深度的脉搏信号，为中医师提供更可靠的脉搏信息。
