Biometrics Research Center, Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong, China.
Med Eng Phys. 2009 Dec;31(10):1283-9. doi: 10.1016/j.medengphy.2009.08.008. Epub 2009 Sep 10.
Wrist pulse signal contains important information about the health status of a person and pulse signal diagnosis has been employed in oriental medicine for thousands of years. In this research, a systematic approach is proposed to analyze the computerized wrist pulse signals, with the focus placed on the feature extraction and pattern classification. The wrist pulse signals are first collected and pre-processed. Considering that a typical pulse signal is composed of periodically systolic and diastolic waves, a modified Gaussian model is adopted to fit the pulse signal and the modeling parameters are then taken as features. Consequently, a feature selection scheme is proposed to eliminate the tightly correlated features and select the disease-sensitive ones. Finally, the selected features are fed to a Fuzzy C-Means (FCM) classifier for pattern classification. The proposed approach is tested on a dataset which includes pulse signals from 100 healthy persons and 88 patients. The results demonstrate the effectiveness of the proposed approach in computerized wrist pulse diagnosis.
腕部脉搏信号包含有关个人健康状况的重要信息，脉搏信号诊断在东方医学中已经使用了数千年。在这项研究中，提出了一种系统的方法来分析计算机化的腕部脉搏信号，重点是特征提取和模式分类。首先采集和预处理腕部脉搏信号。考虑到典型的脉搏信号由周期性的收缩波和舒张波组成，因此采用改进的高斯模型来拟合脉搏信号，然后将建模参数作为特征。因此，提出了一种特征选择方案来消除紧密相关的特征，并选择对疾病敏感的特征。最后，将选择的特征输入到模糊 C 均值（FCM）分类器中进行模式分类。该方法在一个数据集上进行了测试，该数据集包括 100 个健康人和 88 个患者的脉搏信号。结果表明，该方法在计算机化的腕部脉搏诊断中是有效的。
