Hybrid PSO-based variable translation wavelet neural network and its application to hypoglycemia detection system

Publication Type:
Journal Article
Citation:
Neural Computing and Applications, 2013, 23 (7-8), pp. 2177 - 2184
Issue Date:
2013-12-01
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To provide the detection of hypoglycemic episodes in Type 1 diabetes mellitus, hypoglycemia detection system is developed by the use of variable translation wavelet neural network (VTWNN) in this paper. A wavelet neural network with variable translation parameter is selected as a suitable classifier because of its excellent characteristics in capturing nonstationary signal analysis and nonlinear function modeling. Due to the variable translation parameters, the network becomes an adaptive network and provides better classification performance. An improved hybrid particle swarm optimization is used to train the parameters of VTWNN. Using the proposed classifier, a sensitivity of 81.40 % and a specificity of 50.91 % were achieved. The comparison results also show that the proposed detection system performs well in terms of good sensitivity and acceptable specificity. © 2012 Springer-Verlag London Limited.
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