PROJECT TITLE :

Combination of Heterogeneous Features for Wrist Pulse Blood Flow Signal Diagnosis via Multiple Kernel Learning

ABSTRACT :

Wrist pulse signal is of great importance in the analysis of the health standing and pathologic changes of an individual. A number of Feature Extraction methods are proposed to extract linear and nonlinear, and time and frequency options of wrist pulse signal. These options are heterogeneous in nature and are doubtless to contain complementary data, which highlights the necessity for the mixing of heterogeneous features for pulse classification and diagnosis. In this paper, we have a tendency to propose a completely unique effective method to classify the wrist pulse blood flow signals by using the multiple kernel learning (MKL) algorithm to combine multiple types of features. Within the proposed methodology, seven varieties of features are 1st extracted from the wrist pulse blood flow signals using the state-of-the-art pulse feature extraction methods, and are then fed to an efficient MKL methodology, SimpleMKL, to combine heterogeneous features for additional effective classification. Experimental results show that the proposed technique is promising in integrating multiple sorts of pulse features to further enhance the classification performance.