Using high-dimensional features for high-accuracy pulse diagnosis.

Accurate pulse diagnosis is often based on extensive clinical experience. Recently, modern computer-aided pulse diagnostic methods have been developed to help doctors to quickly determine patients' physiological conditions. Most pulse diagnostic methods used low-dimensional feature vectors to classify pulse types. Therefore, some important but subtle pulse information might be ignored. In this study, a novel high-dimensional pulse classification method was developed to improve pulse diagnosis accuracy. To understand the underlying physical meaning or implications hidden in pulse discrimination, 71 pulse features were extracted from the time, spatial, and frequency domains to cover as much pulse information as possible. Then, Principal Component Analysis (PCA) was applied to extract the most representative components. Artificial neural networks were trained to classify 10 different pulse types. The results showed that PCA accounted for 95% of the total variances achieved the highest accuracy of 98.2% in pulse classification. The results also showed that pulse energy, local instantaneous characteristics, main frequency, and waveform complexity were the major factors determining pulse discriminability. This study demonstrated that using high-dimensional features could retain more pulse information and thus, effectively improve pulse diagnostic accuracy.