Pattern recognition of epilepsy using parallel probabilistic neural network

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	Pattern recognition of epilepsy using parallel probabilistic neural network
	Gong C(龚晨)1; Zhou XC(周星辰)2; Niu YY(牛云云)2
刊名	Applied Intelligence
	2021
页码	1
英文摘要	Accurate and rapid pattern recognition of epilepsy from intracranial electroencephalogram (iEEG) recordings is important for medical diagnostics. In this paper, three algorithms based on discrete wavelet transform (DWT) analysis and parallel probabilistic neural network, SA-PNN, SA-PPNN, and LSA-PPNN, are presented to identify iEEG recordings and detect epileptic seizures. Simulated annealing (SA) and local simulated annealing (LSA) are utilized to optimize network parameters of probabilistic neural network classifier, respectively. The combinations of different features are utilized as the input vectors of classifiers to complete classification tasks. Experiments are conducted to deal with five different classification tasks. Compared with non-parallel probabilistic neural network algorithm (SA-PNN), the running time of parallel probabilistic neural network algorithm (SA-PPNN) is shortened by 2.18 times. Compared with SA-PPNN, the average operating time of LSA-PPNN is reduced by 9.97 times. The reason is that LSA-PPNN trains and optimizes parameters with local data firstly and then brings the parameters into the global training data sets to train the network for a test. As the amount of data increases, the superiority over LSA-PPNN is getting more distinct. Our methods are also compared with other existing relative research. Experimental results prove that our methods are much more competitive. In particular, for the classification task C-D, the classification accuracy of our method reaches 83.3%, which is much higher than previous results.
语种	英语
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/52197]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位	1.School of Information Engineering, China University of Geosciences in Beijing, Beijing, 100083, China 2.School of Information Engineering, China University of Geosciences in Beijing, Beijing, 100083, China
推荐引用方式 GB/T 7714	Gong C,Zhou XC,Niu YY. Pattern recognition of epilepsy using parallel probabilistic neural network[J]. Applied Intelligence,2021:1.
APA	Gong C,Zhou XC,&Niu YY.(2021).Pattern recognition of epilepsy using parallel probabilistic neural network.Applied Intelligence,1.
MLA	Gong C,et al."Pattern recognition of epilepsy using parallel probabilistic neural network".Applied Intelligence (2021):1.