Features fusion of multichannel wrist pulse signal based on KL-MGDCCA and decision level combination. (March 2020) - Jiang, Zhixing
Highlights: We propose a features fusion frame base on KL-MGDCCA and decision level combination for multichannel wrist pulse signal analysis. The method can fuse the homogeneous features extracted from different channels by eliminating the redundant correlation information. The method can greatly reduce the length of the features and is suitable for small sample size of wrist pulse database. The decision level fusion is adopted to fuse the classification outputs of heterogeneous features, which can reduce the interference of different types of features on a single classifier. Abstract: To sense the pulse in the representative positions of wrist is the basis of traditional Chinese pulse diagnosis. The pulse diagnosis has been obtaining more and more attentions for its non-invasive character and its convenience in analysis of health status. For objective analysis, various types of pulse features have been extracted from the pulse signal with the development of computerized pulse analysis. The effective utilization of the features of multichannel is the increasing and urgent need for the pulse analysis. A novel features fusion frame is proposed to reduce the redundant information for the homogeneous features, and eliminate the interference of heterogeneous features. For the same type of features extracted from the different channels, the proposed method uses Karhunen–Loeve multiple generalized discriminative canonical correlation analysis (KL-MGDCCA) to fuse them into one feature vector. A support vector machine (SVM) classifier is trained for each type of fused features. Then, the frame adopts decision level fusion approach to combine these classifiers for pulse signal classification to solve the problem of heterogeneous features fusion. Extensive experiments show that the proposed fusion frame can achieve the best performance on most indicators for multichannel pulse signal analysis. For the classification of Diabetes/Health, Nephropathy/Health and Diabetes/Nephropathy, the proposed method achieves the best F -score with the value of 75.25%, 79.02% and 56.54%, which outperforms state-of-the-art methods being compared.