Abstract:
:Electroencephalographic (EEG) signal records the neuronal activity in the brain and it is used in the diagnosis of epileptic seizure activities. Human inspection of non-stationary EEG signal for diagnosing epilepsy is cumbersome, time-consuming and inaccurate. In this paper an effective automatic approach to detect epilepsy using two feature extraction techniques namely local neighbor gradient pattern (LNGP) and symmetrically weighted local neighbor gradient pattern (SWLNGP) are proposed. Extracted features are fed into machine learning algorithms like k-nearest neighbor (k-NN), quadratic linear discriminant analysis, support vector machine, ensemble classifier and artificial neural network (ANN) to classify the EEG signals. In this study, the classification performance for 17 different cases using 10-fold cross validation with the following classification problems are executed (i) healthy-ictal, (ii) interictal-ictal, (iii) healthy-interictal, (iv) seizure free-ictal and (v) healthy-interictal-ictal. The experimental result shows that in all the cases LNGP and SWLNGP attained higher classification accuracy using ANN. Further, the computational performance and the classification accuracy of the proposed methods are compared with the recently proposed techniques for epileptic detection. It shows that the performance of LNGP and SWLNGP method with ANN classifier are superior over other recently proposed techniques for the aforesaid problems. Hence, the proposed methods are simple, fast, reliable and easily implementable for real-time epileptic detection.
journal_name
Australas Phys Eng Sci Medjournal_title
Australasian physical & engineering sciences in medicineauthors
Sairamya NJ,Thomas George S,Balakrishnan R,Subathra MSPdoi
10.1007/s13246-018-0697-9subject
Has Abstractpub_date
2018-12-01 00:00:00pages
1029-1046issue
4eissn
0158-9938issn
1879-5447pii
10.1007/s13246-018-0697-9journal_volume
41pub_type
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