Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis

• Autorzy: Ibrahim S. , Djemal R. , Alsuwailem A.

Identyfikatory

DOI

10.1016/j.bbe.2017.08.006

• Języki publikacji: EN

Abstrakty

EN

Quantification of abnormality in brain signals may reveal brain conditions and pathologies. In this study, we investigate different electroencephalography (EEG) feature extraction and classification techniques to assist in the diagnosis of both epilepsy and autism spectrum disorder (ASD). First, the EEG signal is pre-processed to remove major artifacts before being decomposed into several EEG sub-bands using a discrete-wavelet-transform (DWT). Two nonlinear methods were studied, namely, Shannon entropy and largest Lyapunov exponent, which measure complexity and chaoticity in the EEG recording, in addition to the two conventional methods (namely, standard deviation and band power). We also study the use of a cross-correlation approach to measure synchronization between EEG channels, which may reveal abnormality in communication between brain regions. The extracted features are then classified using several classification methods. Different EEG datasets are used to verify the proposed design exploration techniques: the University of Bonn dataset, the MIT dataset, the King Abdulaziz University dataset, and our own EEG recordings (46 subjects). The combination of DWT, Shannon entropy, and k-nearest neighbor (KNN) techniques produces the most promising classification result, with an overall accuracy of up to 94.6% for the three-class (multi-channel) classification problem. The proposed method obtained better classification accuracy compared to the existing methods and tested using larger and more comprehensive EEG dataset. The proposed method could potentially be used to assist epilepsy and ASD diagnosis therefore improving the speed and the accuracy.

Słowa kluczowe

EN

electroencephalography; computer aided diagnosis; epilepsy; neurological disorder; autism spectrum disorder; machine learning

PL

elektroencefalografia; diagnostyka wspomagana komputerowo; epilepsja; zaburzenia neurologiczne; autyzm; uczenie maszynowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 1
• Strony: 16--26
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wykr.

Twórcy

autor

Ibrahim S.

• Electrical Engineering Department, King Saud University, P.O. Box 800, 11421 Riyadh, Saudi Arabia

autor

Djemal R.

• Electrical Engineering Department, King Saud University, P.O. Box 800, 11421 Riyadh, Saudi Arabia

autor

Alsuwailem A.

• Electrical Engineering Department, King Saud University, P.O. Box 800, 11421 Riyadh, Saudi Arabia

Bibliografia

• [1] Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 2007;31(June–July (4–5)): 198–211.
• [2] Acharya UR, Tree SV, Swapna G, Martis RJ, Suri JS. Automated EEG analysis of epilepsy: a review. Knowl Based Syst 2013;45:147–65.
• [3] Noachtar S, Rémi J. The role of EEG in epilepsy: a critical review. Epilepsy Behav 2009;15(1):22–33.
• [4] Bhat S, Acharya UR, Adeli H, Bairy GM, Adeli A. Automated diagnosis of autism: in search of a mathematical marker. Rev Neurosci 2014;25(6):851–61.
• [5] Adeli H, Ghosh-Dastidar S, Dadmehr N. Alzheimer's disease: models of computation and analysis of EEGs. Clin EEG Neurosci 2005;36(3):131–40.
• [6] Faust O, Acharya UR, Adeli H, Adeli A. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 2015;26(March):56–64.
• [7] AlSharabi K, Ibrahim S, Djemal R, Alsuwailem A. A DWT-entropy-ANN based architecture for epilepsy diagnosis using EEG signals. 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP); 2016. pp. 288–91.
• [8] Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948;27(July–October (3)):379–423.
• [9] Rosenstein MT, Collins JJ, De Luca CJ. A practical method for calculating largest Lyapunov exponents from small data sets. Phys D: Nonlinear Phenom 1993;65(May (1–2)):117–34.
• [10] Quiroga RQ, Kraskov A, Kreuz T, Grassberger P. Performance of different synchronization measures in real data: a case study on electroencephalographic signals. Phys Rev E 2002;65:041903.
• [11] Weinberger KQ, Saul LK. Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 2009;10(February):207–44.
• [12] Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 1998;2:121–67.
• [13] Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory – COLT'92; 1992. p. 144.
• [14] Duda RO, Hart PE, Stork DG. Pattern classification; 2012.
• [15] Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE. Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E 2001;64:061907.
• [16] Goldberger A, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101(23). e215–20. Link to database: http://physionet.org/pn6/chbmit/ [visited 05.09.16].
• [17] Shoeb A. Application of machine learning to epileptic, seizure onset detection and treatment.[Ph.D. thesis] MIT; 2009.
• [18] Alhaddad MJ, Kamel MI, Malibary HM, Alsaggaf EA, Thabit K, Dahlwi F, et al. Diagnosis autism by Fisher linear discriminant analysis FLDA via EEG. Int J Bio-Sci Bio- Technol 2012;4:45–54.
• [19] g.BCIsys – g.tec's Brain-Computer Interface research environment. http://www.gtec.at/Products/Complete-Solutions/g. BCIsys-Specs-Features [visited 15.02.17].
• [20] Murias M, Webb SJ, Greenson J, Dawson G. Resting state cortical connectivity reflected in EEG coherence in individuals with autism. Biol Psychiatry 2007;62(3):270–3.
• [21] Nigam VP, Graupe D. A neural-network-based detection of epilepsy. Neurol Res 2004;26(6):55–60.
• [22] Kannathal N, Lim CM, Acharya UR, Sadasivan PK. Entropies for detection of epilepsy in EEG. Comput Methods Programs Biomed 2005;80(3):187–94.
• [23] Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 2007;32(4):1084–93.
• [24] Srinivasan V, Eswaran C, Sriraam N. Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed 2007;11(3):288–95.
• [25] Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 2009;36(2):2027–36.
• [26] Dhiman R, Saini JS, Priyanka. Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures. Appl Soft Comput 2014;19(June):8–17.
• [27] Bosl W, Tierney A, Tager-Flusberg H, Nelson C. EEG complexity as a biomarker for autism spectrum disorder risk. BMC Med 2011;9(1):1–18.
• [28] Ahmadlou H, Adeli H, Adeli A. Fuzzy synchronization likelihood-wavelet methodology for diagnosis of autism spectrum disorder. J Neurosci Methods 2012;211(2): 203–19.
• [29] Sheikhani A, Behnam H, Mohammadi MR, Noroozian M, Mohammadi M. Detection of abnormalities for diagnosing of children with autism disorders using of quantitative electroencephalography analysis. J Med Syst 2012;36(April (2)):957–63.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).