Zhang David
IEEE J Biomed Health Inform. 2017 Jul;21(4):978-985. doi: 10.1109/JBHI.2016.2628238. Epub 2016 Nov 11.
Traditional Chinese pulse diagnosis, known as an empirical science, depends on the subjective experience. Inconsistent diagnostic results may be obtained among different practitioners. A scientific way of studying the pulse should be to analyze the objectified wrist pulse waveforms. In recent years, many pulse acquisition platforms have been developed with the advances in sensor and computer technology. And the pulse diagnosis using pattern recognition theories is also increasingly attracting attentions. Though many literatures on pulse feature extraction have been published, they just handle the pulse signals as simple 1-D time series and ignore the information within the class. This paper presents a generalized method of pulse feature extraction, extending the feature dimension from 1-D time series to 2-D matrix. The conventional wrist pulse features correspond to a particular case of the generalized models. The proposed method is validated through pattern classification on actual pulse records. Both quantitative and qualitative results relative to the 1-D pulse features are given through diabetes diagnosis. The experimental results show that the generalized 2-D matrix feature is effective in extracting both the periodic and nonperiodic information. And it is practical for wrist pulse analysis.
中医脉诊作为一门经验科学，依赖于主观经验。不同的从业者可能会得出不一致的诊断结果。研究脉象的科学方法应该是分析客观化的腕部脉搏波形。近年来，随着传感器和计算机技术的进步，许多脉搏采集平台得以开发。并且利用模式识别理论进行脉诊也越来越受到关注。尽管已经发表了许多关于脉象特征提取的文献，但它们只是将脉搏信号作为简单的一维时间序列来处理，而忽略了类别内部的信息。本文提出了一种广义的脉象特征提取方法，将特征维度从一维时间序列扩展到二维矩阵。传统的腕部脉象特征对应于广义模型的一个特殊情况。通过对实际脉象记录进行模式分类，验证了所提出的方法。通过糖尿病诊断给出了相对于一维脉象特征的定量和定性结果。实验结果表明，广义二维矩阵特征在提取周期性和非周期性信息方面都是有效的。并且它对于腕部脉象分析是实用的。
