Zhang Weiping, Yang Jingzhi, Fang Yanling, Chen Huanyu, Mao Yihua, Kumar Mohit
Department of Electronic Information Engineering, Nanchang University, 330031 Nanchang, China.
Mprobe Inc., 94303 Palo Alto, USA.
Saudi J Biol Sci. 2017 Mar;24(3):563-573. doi: 10.1016/j.sjbs.2017.01.027. Epub 2017 Jan 25.
The assessment of the physiological state of an individual requires an objective evaluation of biological data while taking into account both measurement noise and uncertainties arising from individual factors. We suggest to represent multi-dimensional medical data by means of an optimal fuzzy membership function. A carefully designed data model is introduced in a completely deterministic framework where uncertain variables are characterized by fuzzy membership functions. The study derives the analytical expressions of fuzzy membership functions on variables of the multivariate data model by maximizing the over-uncertainties-averaged-log-membership values of data samples around an initial guess. The analytical solution lends itself to a practical modeling algorithm facilitating the data classification. The experiments performed on the heartbeat interval data of 20 subjects verified that the proposed method is competing alternative to typically used pattern recognition and machine learning algorithms.
对个体生理状态的评估需要在考虑测量噪声和个体因素引起的不确定性的同时，对生物数据进行客观评估。我们建议通过最优模糊隶属函数来表示多维医学数据。在一个完全确定性的框架中引入了一个精心设计的数据模型，其中不确定变量由模糊隶属函数来表征。该研究通过最大化围绕初始猜测的数据样本的超不确定性平均对数隶属值，推导出多元数据模型变量上模糊隶属函数的解析表达式。该解析解适用于一种实用的建模算法，有助于数据分类。对20名受试者的心跳间期数据进行的实验验证了所提出的方法是通常使用的模式识别和机器学习算法的有力替代方案。
