Wrist pulse recognition based on multi-fractal spectrum

Pulse diagnosis is an important part of the theoretical system of Traditional Chinese Medicine. In this paper we propose a new and an efficient framework to recognize pulse signal in nonlinear angle. Firstly the EEMD (ensemble empirical mode decomposition) method is used to filter out baseline drifting noise, and the result is proved to be effective. Then the MFDFA(multi-fractal detrended fluctuation analysis) method is used to get Hurst index, Renyi index and multi-fractal spectrum. Hurst index is related with the long-range correlations, Renyi index is related with the multi-fractal characteristics, and multi-fractal spectrum contains Δa and Δƒ characteristics. Finally, four kinds of pulse signals are recognized by PSO-SVM after extract multi-fractal spectrum feature. Experiment results demonstrate the effectiveness of our proposed method.