Combination of heterogeneous features for wrist pulse blood flow signal diagnosis via multiple kernel learning

Lei Liu, Wangmeng Zuo, Dapeng Zhang, Naimin Li, Hongzhi Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

47 Citations (Scopus)


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.
Original languageEnglish
Article number6217315
Pages (from-to)598-606
Number of pages9
JournalIEEE Transactions on Information Technology in Biomedicine
Issue number4
Publication statusPublished - 10 Jul 2012


  • Feature extraction
  • multiple kernel learning (MKL)
  • pulse diagnosis
  • wrist pulse blood flow signal

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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