Characterization of Inter-Cycle Variations for Wrist Pulse Diagnosis

Although pulse signal is quasiperiodic, most feature extraction methods usually consider it as a whole or only use a single cycle, neglecting the variations between pulse cycles. To characterize both the inter- and intra-cycle variations, in this chapter we propose three feature extraction methods, i.e., simple combination, multi-scale entropy, and complex network. The simple combination method is a direct extension of conventional single-cycle feature extraction method by concatenating features from multiple cycles. The multi-scale entropy method measures the inter- and intra-cycle variations using entropies of different scales. The complex network method transforms the pulse signal from time domain to network domain and measures the inter-cycle variations using the statistical properties on complex network. Experimental results show that the presented features are effective in characterizing both inter- and intra-cycle variations and can obtain better performance in pulse diagnosis.