Computer Science ›› 2020, Vol. 47 ›› Issue (7): 199-205.doi: 10.11896/jsjkx.200200104

• Artificial Intelligence • Previous Articles     Next Articles

Epileptic EEG Signals Detection Based on Tunable Q-factor Wavelet Transform and Transfer Learning

LUO Ting-rui, JIA Jian, ZHANG Rui   

  1. School of Mathematics,Northwest University,Xi’an 710127,China
    Medical Big Data Research Center,Northwest University,Xi’an 710127,China
  • Received:2020-02-04 Online:2020-07-15 Published:2020-07-16
  • About author:LUO Ting-rui,born in 1996,postgra-duate.Her main research interests include machine learning and medical signal processing.
    JIA Jian,born in 1977,Ph.D,professor.His main research interests include pattern recognition and intelligent information processing.
  • Supported by:
    This work was supported by Innovative Talents Promotion Plan of Shaanxi(2018TD-016) and Key Research and Development Program of Shaanxi(2019ZDLSF02-09-02)

Abstract: Aiming at the detection of epileptic EEG signals,a method of detecting epileptic EEG signals based on Tunable Q-factor wavelet transform and transfer learning is proposed.Firstly,the EEG signals are transformed by Tunable Q-factor wavelet transform,and the subbands with large energy differences are selected for partial reconstruction.The reconstructed signals are rearranged and expressed as two-dimensional color image data.Secondly,through the analysis of the existing automatic seizure detection algorithm and the Xception model of deep separable convolutional networks,the parameters of the pre-training model classified by the ImageNet dataset are used to initialize the network parameters,and the pre-training model of the depth separable convolution network Xception is obtained.Finally,the transfer learning method is used to transfer the pre-training results of the Xception model to the automatic seizure detection task.The performance of this method is verified on the BONN epilepsy dataset,and the accuracy,sensitivity and specificity reaches 99.37%,100% and 98.48%respectively,proving that the model has good generalization ability in automatic seizure detection task.Compared with traditional detection methods and other deep lear-ning methods based,the automatic detection method proposed in this paper achieves higher accuracy,avoids the process of artificial design and feature extraction,and has better application value.

Key words: Automatic detection, Depth separable convolutional network, Epilepsy, Transfer learning, Tunable Q-factor wavelet transform

CLC Number: 

  • TP391.4
[1]HASSAN A R,SIULY S,ZHANG Y.Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating[J].Computer Methods and Programs in Biomedicine,2016(137):247-259.
[2]SHAFIQUE A,SAYEED M,TSAKALIS K.Nonlinear Dynamical Systems with Chaos and Big Data:A Case Study of Epileptic Seizure Prediction and Control[M].Cham:Springer InternationalPublishing,2018.
[3]SHARMA R,PACHORI R B.Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions[J].Expert Systems with Applications,2015,42(3):1106-1117.
[4]PATIDAR S,PANIGRAHI T.Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals[J].Biomedical Signal Processing and Control,2017,34:74-80.
[5]SHARMA M,PACHORI R B,RAJENDRA A U.A new ap-proach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension[J].Pattern Recognition Letters,2017,94:172-179.
[6]PEKER M,SEN B,DELEN D.A Novel Method for Automated Diagnosis of Epilepsy Using Complex-Valued Classifiers[J].IEEE Journal of Biomedical and Health Informatics,2016,20(1):108-118.
[7]SAXENA S,HEMANTH C,SANGEETHA R G.Classification of normal,seizure and seizure-free EEG signals using EMD and EWT[C]//International Conference on Nextgen Electronic Technologies:Silicon toSoftware (ICNETS2).IEEE,2017:360-366.
[8]JIA J,BALAJI,SONG J L,et al.Automated identification of epi-leptic seizures in EEG signals based on phase space representation and statistical features in the CEEMDdomain[J].Biome-dical Signal Processing and Control,2017,38:148-157.
[9]RIAZ F,HASSAN A,REHMAN S,et al.EMD-Based Temporal and Spectral Featuresfor the Classification of EEG SignalsUsing Supervised Learning[J].IEEE transactions on neural systems and rehabilitation engineering:a publication of the IEEE Engineering in Medicine and Biology Society,2016,24(1):28-35.
[10]MUTLU A Y.Detection of epileptic dysfunctions in EEG signals using Hilbert vibration decomposition[J].Biomedical Signal Processing and Control,2018,40:33-40.
[11]LU Y,MA Y,CHEN C,et al.Classification of single-channel EEG signals for epileptic seizures detection based on hybrid features[J].Technology and Health Care,2018,26(S1):337-346.
[12]HE K,ZHANG X,REN S,et al.Deep residual learning for ima-ge recognition[C]//Proc.of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[13]SELESNICK,IVAN W.Wavelet Transform with Tunable Q-Factor[J].IEEE Transactions on Signal Processing,2011,59(8):3560-3575.
[14]BAYRAM I,SELESNICK I W.Frequencydomain design ofovercomplete rational-dilation wavelet transforms[J].IEEE Transactions on Signal Processing,2009,57(8):2957-2972.
[15]CHEN W Z,WANG X X,ZHANG T.Research of Discrimination Between Left and Right Hand Motor ImageryEEG Patterns Based on Tunable Q-Factor Wavelet Transform[J].Journal of Electronics & Information Technology,2019,41(3):530-536.
[16]KRIZHEVSKY A,SUTSKEVER I,HINTON G.ImageNetClassification with Deep Convolutional Neural Networks[J].Advancesin Neural Information Processing Systems,2012,25(2):1097-1105.
[17]ZHANG Z Z,GAO J Y,LV G,et al.Pathological Image Classification of Gastric Cancer Based on Depth Learning[J].ComputerScience,2018,45(S2):263-268.
[18]YU J Y,DING P C,WANG C.Overview:Application of Convolutional Neural Network in Object Detection [J].Computer Science,2018,45(S2):17-26.
[19]WANG H L,QI X L,WU G S.Research Progress of Object Detection Technology Based on Convolutional Neural Network in Deep Learning[J].Computer Science,2018,45(9):11-19.
[20]ZHOU F Y,JIN L P,DONG J.Review of convolutional neuralnetwork[J].Chinese Journal of Computers,2017,40(6):1229-1251.
[21]SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the inception architecture for computer vision[C]//Proc.of the IEEE Conference on Computer Vision and Pattern Recognition.2016:2818-2826.
[22]CHOLLET F.Xception:Deep learning with depth wise separable convolutions[C]//Proc.of the IEEE Conference onCompu-ter Visionand Pattern Recognition.2017:1251-1258.
[23]HU H G,KONG X Y,ZHOU Q W,et al.Melanoma Classification Method by Integrating Deep Convolutional Residual Network [J].Computer Science,2019,46(5):247-253.
[24]TAJBAKHSH N,SHIN J Y,GURUDU S R,et al.Convolutio-nal Neural Networks for Medical Image Analysis:Fine Tuning or Full Training? [J].IEEE Transactions on Medical Imaging,2016,35(5):1299-1312.
[25]JIA D,WEI D,SOCHER R,et al.ImageNet:A large-scale hie-rarchical image database[C]//Proc of IEEE Computer Vision & Pattern Recognition.2009:248-255.
[26]ANDRZEJAK R G,LEHNERTZ K,MORMANN F,et al.Indications of nonlinear deterministic and finite-dimensional structuresin time series of brain electrical activity:Dependence on recording region and brain state[J].Physical Review E,2001,64(6):061907.
[27]POWERS D M W.Visualization of tradeoffin evaluation:fromprecision-recall & PN to LIFT,ROC & BIRD[J].arXiv:1505.00401,2015.
[28]HASSAN A R,SUBASI A.Automatic identification of epileptic
seizures from EEG signals using linear programming boosting[J].Computer Methods and Programs in Biomedicine,2016,136:65-77.
[29]KUMAR Y,DEWAL M L,ANAND R S.Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network[J].Signal,Image and Video Processing,2014,8(7):1323-1334.
[30]SAMIEE K,KOVACS P,GABBOUJ M.Epileptic Seizure Classification of EEG TimeSeries Using Rational Discrete Short-Time Fourier Transform[J].IEEE Transactions onBiomedical Engineering,2015,62(2):541-552.
[31]AHMEDT-ARISTIZABAL D,FOOKES C,NGUYEN K,et al.Deep classification of epileptic signals[C]//2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).IEEE,2018:332-335.
[32]ZHANG J,WU H,SU W,et al.A New Approach for Classification of Epilepsy EEG Signals Based on Temporal Convolutional Neural Networks[C]//11th International Symposium on Computational Intelligence and Design (ISCID).IEEE,2018:80-84.
[33]ABBASI M U,RASHAD A,BASALAMAH A,et al.Detection of Epilepsy Seizures in Neo-Natal EEG Using LSTM Architecture[J].IEEE Access,2019,7:179074-17908.
[34]HE W P,YANG Y,et al.Tunable Q-factor wavelet transform denoising with neighboring coefficients and its application to rotating machinery fault diagnosis[J].Science China (Technological Sciences),2013(8):126-135.
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