文献类型：期刊文献

英文题名：Bayesian classifier with multivariate distribution based on D-vine copula model for awake/drowsiness interpretation during power nap

作者：Wang, Bei[1];Sun, Yudong[1];Zhang, Tao[2];Sugi, Takenao[3];Wang, Xingyu[1]

机构：[1]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, 130 Meilong St, Shanghai 200237, Peoples R China;[2]Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China;[3]Saga Univ, Fac Sci & Engn, Dept Elect & Elect Engn, Saga 8408502, Japan

年份：2020

卷号：56

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20194207539037);WOS:【SCI-EXPANDED(收录号:WOS:000501411100013)】；

基金：This study is supported by the National Natural Science Foundation of China under Grants 61773164, the National Key Research and Development Program of China2017YFB13003002, and the Natural Science Foundation of Shanghai under Grant 16ZR1407500.

语种：英文

外文关键词：Bayesian classifier; Multivariate distribution; D-vine copula; Drowsiness; Power nap

摘要：In this study, a Bayesian classifier with the multivariate distribution based on the D-vine copula model is developed and evaluated for the awake/drowsiness interpretation during the power nap. The objective is to consider the correlation among the features into the automatic classification algorithm. A power nap is a short sleep process, which is commonly considered as a supplement to the insufficient overnight sleep. It may involve the states of awake and drowsiness. Neurophysiological features are extracted from the EEGs (electroencephalography) and EOGs (electrooculography), which are synchronously recorded during one's short nap after lunch. The multivariate distribution of features is decomposed into independency and dependency products according to the D-vine copula model. The independency product is the marginal probability density function of the features. The dependency product consists of pair-copula functions. The marginal probability density is estimated by the kernel function and k-nearest-neighbor density respectively. The parameters of pair-copula functions are estimated by the maximum likelihood estimation. In total, 8 healthy subjects were involved. The comparison results showed that the Bayesian classifier with the multivariate distribution based on the D-vine copula model obtained quite satisfied classification accuracy. The developed method introduced a feasible way to construct the multivariate distribution, which can enhance the classification performance of Bayesian classifier when dealing with the complex correlation of features in actual cases. (C) 2019 Elsevier Ltd. All rights reserved.