Wrist pulse signal diagnosis using modified Gaussian models and Fuzzy C-Means classification.

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.