Computerized Wrist Pulse Signal Diagnosis Using KPCA

Abstract

Wrist pulse signals can reflect the pathological changes of a person's body condition due to the richness and importance of the contained information. In recent years, the computerized pulse signal analysis has shown a great potential to the modernization of traditional pulse diagnosis. In this paper, we attempted to use the wrist pulse signals collected by a Doppler ultrasonic blood analyzer to perform wrist pulse signal diagnosis. We first cropped the wrist pulse signal to obtain the single-period waveform, and then employed KPCA to extract features from the waveform. Finally, we used a nearest neighborhood classifier to classify the extracted features. We adopted a wrist pulse signal dataset, which includes pulse signals from both healthy persons and patients. Several experiments on the dataset were carried out and the results show that our developed approach is feasible for computerized wrist pulse diagnosis.