뇌파의 중첩 분할에 기반한 CNN 앙상블 모델을 이용한 뇌전증 발작 검출 = Epileptic Seizure Detection Using CNN Ensemble Models Based on Overlapping Segments of EEG Signals

다국어 초록 (Multilingual Abstract)

As the diagnosis using encephalography(EEG) has been expanded, various studies have been actively performed for classifying EEG automatically. This paper proposes a CNN model that can effectively classify EEG signals acquired from healthy persons and patients with epilepsy. We segment the EEG signals into sub-signals with smaller dimension to augment the EEG data that is necessary to train the CNN model. Then the sub-signals are segmented again with overlap and they are used for training the CNN model. We also propose ensemble strategy in order to improve the classification accuracy. Experimental result using public Bonn dataset shows that the CNN can detect the epileptic seizure with the accuracy above 99.0%. It also shows that the ensemble method improves the accuracy of 3-class and 5-class EEG classification.

참고문헌 (Reference)

1 N. Kumar, "Wavelet transform for classification of EEG signal using SVM and ANN" 10 (10): 2061-2069, 2017

2 P. Sandheep, "Performance analysis of deep learning CNN in classification of depression EEG signals" 1339-1344, 2019

3 R. G. Andrzejak, "Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity : Dependence on recording region and brain state" 64 (64): 1-8, 2001

4 K. K. Ang, "Filter bank common spatial pattern(FBCSP)in brain-computer interface" 2390-2397, 2008

5 "Epileptic Seizure Recognition Data Set"

6 K. Jung, "Epidemiology of epilepsy in Korea" 2 (2): 17-20, 2020

7 D. J. McFarland, "EEG-based brain-computer interfaces" 4 : 194-200, 2017

8 W. Mao, "EEG dataset classification using CNN method" 1456 : 1-7, 2020

9 A. Demerdzieva, "EEG characteristics of generalized anxiety disorder in childhood" 19 (19): 9-15, 2011

10 Mohammad Reza Mohammadi, "EEG Classification of ADHD and Normal Children Using Non-linear Features and Neural Network" 대한의용생체공학회 6 (6): 66-73, 2016

11 K. C. Hsu, "Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm" 40 (40): 823-830, 2010

12 S. Bavkar, "Detection of alcoholism : An EEG hybrid features and ensemble subspace K-NN based approach" Springer 161-168, 2019

13 R. T. Schirrmeister, "Deep learning with convolutional neural networks for EEG decoding and visualization" 38 (38): 5391-5420, 2017

14 U. R. Acharya, "Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals" 100 : 270-278, 2018

15 R. A. Ricardo, "Analysis of EEG signal processing techniques based on spectrograms" 145 : 151-162, 2017

16 I. Ullah, "An automated system for epilepsy detection using EEG brain signals based on deep learning approach" 107 : 61-71, 2018

17 G. Xu, "A one-dimensional CNN-LSTM model for epileptic seizure recognition using EEG signal analysis" 14 : 1-9, 2020

18 A. Bhattacharyya, "A multivariate approach for patient specific EEG seizure detection using empirical wavelet transform" 64 (64): 2003-2015, 2017

19 M. Savadkoohi, "A machine learning approach to epileptic seizure prediction using Electroencephalogram(EEG)signal" 40 (40): 1328-1341, 2020