An Analysis of Integration of Hill Climbing in Crossover and Mutation operation for EEG Signal Classification

被引:11
作者
Bhardwaj, Arpit [1 ]
Tiwari, Aruna [1 ]
Varma, M. Vishaal [1 ]
Krishna, M. Ramesh [1 ]
机构
[1] Indian Inst Technol Indore, Indore, Madhya Pradesh, India
来源
GECCO'15: PROCEEDINGS OF THE 2015 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2015年
关键词
Genetic Programming; Epilepsy; Crossover; Mutation; Fitness Function; Hill Climbing Search; EPILEPTIC SEIZURE DETECTION; TIME-SERIES;
D O I
10.1145/2739480.2754710
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
A common problem in the diagnosis of epilepsy is the volatile and unpredictable nature of the epileptic seizures. Hence, it is essential to develop Automatic seizure detection methods. Genetic programming (GP) has a potential for accurately predicting a seizure in an EEG signal. However, the destructive nature of crossover operator in GP decreases the accuracy of predicting the onset of a seizure. Designing constructive crossover and mutation operators (CCM) and integrating local hill climbing search technique with the GP have been put forward as solutions. In this paper, we proposed a hybrid crossover and mutation operator, which uses both the standard GP and CCM-GP, to choose high performing individuals in the least possible time. To demonstrate our approach, we tested it on a benchmark EEG signal dataset. We also compared and analyzed the proposed hybrid crossover and mutation operation with the other state of art GP methods in terms of accuracy and training time. Our method has shown remarkable classification results. These results affirm the potential use of our method for accurately predicting epileptic seizures in an EEG signal and hint on the possibility of building a real time automatic seizure detection system.
引用
收藏
页码:209 / 216
页数:8
相关论文
共 30 条
[1]   Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state [J].
Andrzejak, RG ;
Lehnertz, K ;
Mormann, F ;
Rieke, C ;
David, P ;
Elger, CE .
PHYSICAL REVIEW E, 2001, 64 (06) :8-061907
[2]  
[Anonymous], 2008, EEG signal processing
[3]  
[Anonymous], 2006, COMPUT SCI, DOI DOI 10.4018/JDWM.2007070101
[4]  
[Anonymous], 1999, The biology of mind. Origins and structures of mind, brain and consciousness
[5]  
[Anonymous], 1992, GENETIC PROGRAMMING
[6]   A radial basis function neural network model for classification of epilepsy using EEG signals [J].
Aslan, Kezban ;
Bozdemir, Hacer ;
Sahin, Cenk ;
Ogulata, Seyfettin Noyan ;
Erol, Rizvan .
JOURNAL OF MEDICAL SYSTEMS, 2008, 32 (05) :403-408
[7]   Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition [J].
Bajaj, Varun ;
Pachori, Ram Bilas .
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2012, 16 (06) :1135-1142
[8]  
Bhardwaj A.Tiwari., 2014, Proceedings of Genetic and Evolutionary Computation Conference (GECCO, 2014), P1297
[9]   The use of the area under the roc curve in the evaluation of machine learning algorithms [J].
Bradley, AP .
PATTERN RECOGNITION, 1997, 30 (07) :1145-1159
[10]   Semantic Search-Based Genetic Programming and the Effect of Intron Deletion [J].
Castelli, Mauro ;
Vanneschi, Leonardo ;
Silva, Sara ;
Agapitos, Alexandros ;
O'Neill, Michael .
IEEE TRANSACTIONS ON CYBERNETICS, 2014, 44 (01) :103-113