Epileptic seizure prediction using scalp electroencephalogram signals

• Autorzy: Usman Syed Muhammad , Khalid Shehzad , Bashir Zafar

Identyfikatory

DOI

10.1016/j.bbe.2021.01.001

• Języki publikacji: EN

Abstrakty

EN

Epilepsy is a brain disorder in which patients undergo frequent seizures. Around 30% of patients affected with epilepsy cannot be treated with medicines/surgical procedures. Abnormal activity, known as the preictal state starts few minutes before the seizure actually occurs. Therefore, it may be possible to deliver medication prior to the occurrence of a seizure if initiation of the preictal state can predicted before the seizure onset. We propose an epileptic seizure prediction method that predicts the preictal state before the seizure onset using electroencephalogram (EEG) monitoring of brain activity. It involves three steps including preprocessing of EEG signals, feature extraction classification of preictal and interictal states. In our proposed method, we have used (i) Empirical model decomposition to remove noise from the EEG signals and Generative Adversarial Networks to generate preictal samples to deal with the class imbalance problem; (ii) Automated features have been extracted with three layer Convolutional Neural Networks and (iii) Classification between preictal and interictal states is done with Long Short Term Memory units. In this study, we have used CHBMIT dataset of scalp EEG signals and have validated our proposed method on 22 subjects of dataset. Our proposed seizure prediction method is able to achieve 93% sensitivity and 92.5% specificity with average time of 32 min to predict the seizure's onset. Results obtained from our method have been compared with recent state-of-the-art epileptic seizure prediction methods. Our proposed method performs better in terms of sensitivity, specificity and average anticipation time.

Słowa kluczowe

EN

epilepsy prediction; preictal EEG; interictal EEG; convolutional neural network; CNN; long short term memory units; LSTMs; generative adversarial network; GAN; empirical mode decomposition; EMD

PL

przewidywanie napadu padaczkowego; elektroencefalogram; sieć neuronowa konwolucyjna; empiryczna dekompozycja sygnału

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2021
• Tom: Vol. 41, no. 1
• Strony: 211--220
• Opis fizyczny: Bibliogr. 62 poz., rys., tab., wykr.

Twórcy

autor

Usman Syed Muhammad

• Department of Computer Engineering, Bahria University, Islamabad, Pakistan; Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan

autor

Khalid Shehzad

• Department of Computer Engineering, Bahria University, Islamabad, Pakistan

autor

Bashir Zafar

• School of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, UK

Bibliografia

• [1] Moshcé SL, Perucca E, Ryvlin P, Tomson T. Epilepsy: new advances. Lancet 2015;385(9971):884–98.
• [2] Devarajan K, Jyostna E, Jayasri K, Balasampath V. Eeg-based epilepsy detection and prediction. Int J Eng Technol 2014;6 (3):212–6.
• [3] Cook MJ, O'Brien TJ, Berkovic SF, Murphy M, Morokoff A, Fabinyi G, et al. Prediction of seizure likelihood with a longterm, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol 2013;12(6):563–71.
• [4] Abdulghani AM, Casson AJ, Rodriguez-Villegas E. Compressive sensing scalp eeg signals: implementations and practical performance. Med Biol Eng Comput 2012;50(11):1137–45.
• [5] Chávez M, Martinerie J, Le Van Quyen M. Statistical assessment of nonlinear causality: application to epileptic eeg signals. J Neurosci Methods 2003;124(2):113–28.
• [6] Robinson P, Rennie C, Rowe D. Dynamics of large-scale brain activity in normal arousal states and epileptic seizures. Phys Rev E 2002;65(4):041924.
• [7] Schuyler R, White A, Staley K, Cios KJ. Epileptic seizure detection. IEEE Eng Med Biol Mag 2007;26(2):74.
• [8] Fisher RS, Engel Jr JJ. Definition of the postictal state: when does it start and end? Epilepsy Behav 2010;19(2):100–4.
• [9] Ghosh-Dastidar S, Adeli H, Dadmehr N. Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans Biomed Eng 2007;54(9):1545–51.
• [10] Iasemidis LD. Epileptic seizure prediction and control. IEEE Trans Biomed Eng 2003;50(5):549–58.
• [11] Yu H, Fan C, Zhang Y. Epilepsy detection in eeg using grassmann discriminant analysis method. Comput Math Methods Med 2020.
• [12] Nasseri M, Kremen V, Nejedly P, Kim I, Chang SY, Jo HJ, et al. Semi-supervised training data selection improves seizure forecasting in canines with epilepsy. Biomed Signal Process Control 2020;57:101743.
• [13] Das K, Daschakladar D, Roy PP, Chatterjee A, Saha SP. Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of eeg signal. Biomed Signal Process Control 2020;57:101720.
• [14] Ozcan AR, Erturk S. Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Trans Neural Syst Rehabil Eng 2019;27 (11):2284–93.
• [15] Tsipouras MG. Spectral information of eeg signals with respect to epilepsy classification. EURASIP J Adv Signal Process 2019;2019(1):10.
• [16] Usman SM, Hassan A. Efficient prediction and classification of epileptic seizures using eeg data based on univariate linear features. JCP 2018;13(6):616–21.
• [17] Sudalaimani C, Sivakumaran N, Elizabeth TT, Rominus VS. Automated detection of the preseizure state in eeg signal using neural networks. Biocybern Biomed Eng 2019;39 (1):160–75.
• [18] Quintero-Rincón A, Pereyra M, d'Giano C, Risk M, Batatia H. Fast statistical model-based classification of epileptic eeg signals. Biocybern Biomed Eng 2018;38(4):877–89.
• [19] Zhou M, Tian C, Cao R, Wang B, Niu Y, Hu T, et al. Epileptic seizure detection based on eeg signals and cnn. Front Neuroinform 2018;12:95.
• [20] Ibrahim S, Djemal R, Alsuwailem A. Electroencephalography (eeg) signal processing for epilepsy and autism spectrum disorder diagnosis. Biocybern Biomed Eng 2018;38(1):16–26.
• [21] Zhang T, Chen W, Li M. Generalized stockwell transform and svd-based epileptic seizure detection in eeg using random forest. Biocybern Biomed Eng 2018;38(3):519–34.
• [22] Yavuz E, Kasapbasi MC, Eyüpoglu C, Yazici R. An epileptic seizure detection system based on cepstral analysis and generalized regression neural network. Biocybern Biomed Eng 2018;38(2):201–16.
• [23] Li M, Chen W, Zhang T. Application of modwt and lognormal distribution model for automatic epilepsy identification. Biocybern Biomed Eng 2017;37(4):679–89.
• [24] Swami P, Gandhi TK, Panigrahi BK, Tripathi M, Anand S. A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst Appl 2016;56:116–30.
• [25] Usman SM, Khalid S, Akhtar R, Bortolotto Z, Bashir Z, Qiu H. Using scalp eeg and intracranial eeg signals for predicting epileptic seizures: review of available methodologies. Seizure 2019;71:258–69.
• [26] Zhang A, Yang B, Huang L. Feature extraction of eeg signals using power spectral entropy. 2008 International Conference on BioMedical Engineering and Informatics, vol. 2; 2008. pp. 435–9.
• [27] Cui S, Duan L, Qiao Y, Xiao Y. Learning eeg synchronization patterns for epileptic seizure prediction using bag-of-wave features. J Ambient Intell Humaniz Comput 2018;1–16.
• [28] Büyükçakir B, Elmaz F, Mutlu AY. Hilbert vibration decomposition-based epileptic seizure prediction with neural network. Comput Biol Med 2020;119:103665.
• [29] Liu CL, Xiao B, Hsaio WH, Tseng VS. Epileptic seizure prediction with multi-view convolutional neural networks. IEEE Access 2019;7:170352–61.
• [30] Wei X, Zhou L, Zhang Z, Chen Z, Zhou Y. Early prediction of epileptic seizures using a long-term recurrent convolutional network. J Neurosci Methods 2019;327:108395.
• [31] Alshebeili SA, Sedik A, Abd El-Rahiem B, Alotaiby TN, El Banby GM, El-Khobby HA, et al. Inspection of eeg signals for efficient seizure prediction. Appl Acoust 2020;166:107327.
• [32] Chu H, Chung CK, Jeong W, Cho KH. Predicting epileptic seizures from scalp eeg based on attractor state analysis. Comput Methods Programs Biomed 2017;143:75–87.
• [33] Truong ND, Nguyen AD, Kuhlmann L, Bonyadi MR, Yang J, Ippolito S, et al. Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw 2018;105:104–11.
• [34] Khan H, Marcuse L, Fields M, Swann K, Yener B. Focal onset seizure prediction using convolutional networks. IEEE Trans Biomed Eng 2017;65(9):2109–18.
• [35] Fei K, Wang W, Yang Q, Tang S. Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 2017;249:290–8.
• [36] Ibrahim F, El-Gindy SAE, El-Dolil SM, El-Fishawy AS, ElRabaie ESM, Dessouky MI, et al. A statistical framework for eeg channel selection and seizure prediction on mobile. Int J Speech Technol 2019;22(1):191–203.
• [37] Alotaiby TN, Alshebeili SA, Alotaibi FM, Alrshoud SR. Epileptic seizure prediction using csp and lda for scalp eeg signals. Comput Intell Neurosci 2017;2017.
• [38] Cho D, Min B, Kim J, Lee B. Eeg-based prediction of epileptic seizures using phase synchronization elicited from noiseassisted multivariate empirical mode decomposition. IEEE Trans Neural Syst Rehabil Eng 2016;25(8):1309–18.
• [39] Myers MH, Padmanabha A, Hossain G, de Jongh Curry AL, Blaha CD. Seizure prediction and detection via phase and amplitude lock values. Front Hum Neurosci 2016;10:80.
• [40] Rasekhi J, Mollaei MRK, Bandarabadi M, Teixeira CA, Dourado A. Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. J Neurosci Methods 2013;217(1–2):9–16.
• [41] Usman SM, Khalid S, Aslam MH. Epileptic seizures prediction using deep learning techniques. IEEE Access 2020;8:39998–40007.
• [42] Direito B, Ventura F, Teixeira C, Dourado A. Optimized feature subsets for epileptic seizure prediction studies. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2011. pp. 1636–9.
• [43] Zandi AS, Dumont GA, Javidan M, Tafreshi R. An entropybased approach to predict seizures in temporal lobe epilepsy using scalp eeg. 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2009. pp. 228–31.
• [44] Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H. Deep convolutional neural network for the automated detection and diagnosis of seizure using eeg signals. Comput Biol Med 2018;100:270–8.
• [45] Savadkoohi M, Oladduni T. A machine learning approach to epileptic seizure prediction using electroencephalogram (eeg) signal. Biocybern Biomed Eng 2020.
• [46] Mamli S, Kalbkhani H. Gray-level co-occurrence matrix of fourier synchro-squeezed transform for epileptic seizure detection. Biocybern Biomed Eng 2019;39(1):87–99.
• [47] Bajaj V, Pachori RB. Classification of seizure and nonseizure eeg signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 2011;16(6):1135–42.
• [48] Martis RJ, Acharya UR, Tan JH, Petznick A, Yanti R, Chua CK, et al. Application of empirical mode decomposition (emd) for automated detection of epilepsy using eeg signals. Int J Neural Syst 2012;22(06):1250027.
• [49] Sweeney-Reed C, Andrade A, Nasuto S. Empirical mode decomposition of eeg signals for synchronisation analysis. IEEE Engineering in Medicine and Biology Student Society (EMBSS) UKRI Postgraduate Conference on Biomedical Engineering and Medical Physics 2004;15–6.
• [50] Peters BO, Pfurtscheller G, Flyvbjerg H. Automatic differentiation of multichannel eeg signals. IEEE Trans Biomed Eng 2001;48(1):111–6.\
• [51] Sudalaimani C, Thomas TE, Sivakumaran N, Valsalam SR, Meethal SP, Sathish E. Seizure prediction using general regression neural network. 2017 Trends in Industrial Measurement and Automation (TIMA); 2017. pp. 1–7.
• [52] de Jongh A, de Munck JC, Gonçalves SI, Ossenblok P. Differences in meg/eeg epileptic spike yields explained by regional differences in signal-to-noise ratios. J Clin Neurophysiol 2005;22(2):153–8.
• [53] Usman SM, Usman M, Fong S. Epileptic seizures prediction using machine learning methods. Comput Math Methods Med 2017 2017.
• [54] Polat K, Günes S. Classification of epileptiform eeg using a hybrid system based on decision tree classifier and fast fourier transform. Appl Math Comput 2007;187(2):1017–26.
• [55] Meenakshi D, Singh A, Singh A. Frequency analysis of healthy & epileptic seizure in eeg using fast fourier transform. Int J Eng Res Gen Sci 2014;2(4):683–91.
• [56] Li G, Lee CH, Jung JJ, Youn YC, Camacho D. Deep learning for eeg data analytics: a survey. Concurr Comput: Pract Exp 2020;32(18):e5199.
• [57] Pachori RB, Patidar S. Epileptic seizure classification in eeg signals using second-order difference plot of intrinsic mode functions. Comput Methods Programs Biomed 2014;113 (2):494–502.
• [58] Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, et al. Niftynet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed 2018;158:113–22.
• [59] Pachori RB, Bajaj V. Analysis of normal and epileptic seizure eeg signals using empirical mode decomposition. Comput Methods Programs Biomed 2011;104(3):373–81.
• [60] Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP. Automated eeg-based screening of depression using deep convolutional neural network. Comput Methods Programs Biomed 2018;161:103–13.
• [61] Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR. A new approach for arrhythmia classification using deep coded features and lstm networks. Comput Methods Programs Biomed 2019;176:121–33.
• [62] Stollenga MF, Byeon W, Liwicki M, Schmidhuber J. Parallel multi-dimensional lstm, with application to fast biomedical volumetric image segmentation. Advances in neural information processing systems. 2015;2998-3006.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).