Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/8812
Title: Combination of heterogeneous features for wrist pulse blood flow signal diagnosis via multiple kernel learning
Authors: Liu, L
Zuo, W
Zhang, D 
Li, N
Zhang, H
Keywords: Feature extraction
Multiple kernel learning (MKL)
Pulse diagnosis
Wrist pulse blood flow signal
Issue Date: 2012
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on information technology in biomedicine, 2012, v. 16, no. 4, 6217315, p. 598-606 How to cite?
Journal: IEEE transactions on information technology in biomedicine 
Abstract: Wrist pulse signal is of great importance in the analysis of the health status and pathologic changes of a person. A number of feature extraction methods have been proposed to extract linear and nonlinear, and time and frequency features of wrist pulse signal. These features are heterogeneous in nature and are likely to contain complementary information, which highlights the need for the integration of heterogeneous features for pulse classification and diagnosis. In this paper, we propose a novel effective method to classify the wrist pulse blood flow signals by using the multiple kernel learning (MKL) algorithm to combine multiple types of features. In the proposed method, seven types of features are first 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 method, SimpleMKL, to combine heterogeneous features for more effective classification. Experimental results show that the proposed method is promising in integrating multiple types of pulse features to further enhance the classification performance.
URI: http://hdl.handle.net/10397/8812
ISSN: 1089-7771
DOI: 10.1109/TITB.2012.2195188
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