Ictal EEG classification based on amplitude and frequency contours of IMFs

• Autorzy: Biju K. S. , Hakkim H. A. , Jibukumar M. G.

Identyfikatory

DOI

10.1016/j.bbe.2016.12.005

• Języki publikacji: EN

Abstrakty

EN

Electroencephalogram (EEG) signal serves is a powerful tool in epilepsy detection. This study decomposes intrinsic mode functions (IMFs) into amplitude envelope and frequency functions on a time-scale basis using the analytic function of Hilbert transform. IMFs results from the empirical mode decomposition of EEG signals. Features such as energy and entropy parameters were calculated from the amplitude contour of each IMF. Other features, such as interquartile range, mean absolute deviation and standard deviation are also computed for their instantaneous frequencies. Discriminative features were extracted using a large data-base to classify healthy and ictal EEG signals. Normal EEG segments were differentiated from the seizure attack in individual IMF features, multiple features with individual IMF, and individual features with multiple IMFs. Discriminating capability of three Cases was tested. (i) Artificial neural network and (ii) adaptive neuro-fuzzy inference system classification were used to identify EEG segments with seizure attacks. ANOVA was used to analyze statistical performance. Energy and entropy-based features of instantaneous amplitude and standard deviation of instantaneous frequency of IMF2 and IMF1 have 100% accuracy, sensitivity, and specificity. Good performance with a single feature that represents information of the whole data was obtained. The result involved less complicated computation than other time– frequency analysis techniques.

Słowa kluczowe

EN

artificial neural network; ANOVA; electroencephalogram signal; EMD; HHT; seizure

PL

sztuczna sieć neuronowa; ANOVA; elektroencefalogram; EMD; HHT

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2017
• Tom: Vol. 37, no. 1
• Strony: 172--183
• Opis fizyczny: Bibliogr. 58 poz., rys., tab., wykr.

Twórcy

autor

Biju K. S.

• Electronics Engineering Division, School of Engineering, Cochin University of Science & Technology, Kochi, 682022, India

autor

Hakkim H. A.

• Electronics and Communication Engineering Department, Government Engineering College, Bartonhill, Thiruvananthapuram, India

autor

Jibukumar M. G.

• Electronics Engineering Division, School of Engineering, Cochin University of Science & Technology, Kochi, 682022, India

Bibliografia

• [1] Subasi A, Alkan A, Koklukaya E, Kiymik MK. Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing. Neural Networks 2005;18(7):985–97.
• [2] Sanei S, Chambers JA. EEG signal processing. Singapore: John Wiley & Sons; 2013.
• [3] Fauci WA, Braunwald E, Kasper D, Hauser S, Longo D, Jameson J. Harrison principle of internal medicine. 18th ed. USA: McGraw-Hill; 2008.
• [4] Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS. Automated EEG analysis of epilepsy: a review. Knowledge- Based Systems 2013;45:147–65.
• [5] Alotaiby TN, Alshebeili SA, Alshawi T, Ahmad I, El-Samie FE. EEG seizure detection and prediction algorithms: a survey. EURASIP J Adv Signal Process 2014;2014(1):1–21.
• [6] Parvez MZ, Paul M. Epileptic seizure detection by analyzing EEG signals using different transformation techniques. Neurocomputing 2014;145(5):190–200.
• [7] Polat K, Güneş 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.
• [8] Hopfengärtner R, Kasper BS, Graf W, Gollwitzer S, Kreiselmeyer G, Stefan H, et al. Automatic seizure detection in long-term scalp EEG using an adaptive thresholding technique: a validation study for clinical routine. Clin Neurophysiol 2014;125(7):1346–52.
• [9] Adeli H, Zhou Z, Dadmehr N. Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 2003;123(1):69–87.
• [10] Zandi AS, Javidan M, Dumont GA, Tafreshi R. Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Trans Biomed Eng 2010;57(7):1639–51.
• [11] Faust O, Acharya UR, Adeli H, Adeli A. Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis. Seizure 2015;26:56–64.
• [12] Geng D, Zhou W, Zhang Y, Geng S. Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG. Biocyber Biomed Eng 2016;36 (2):375–84.
• [13] Sharma M, Dhere A, Pachori RB, Acharya UR. An automatic detection of focal eeg signals using new class of time– frequency localized orthogonal wavelet filter banks. Knowledge-Based Systems. Available online 30 Nov 2016 (Unpublished results).
• [14] Acharya UR, Fujita H, Sudarshan VK, Bhat S, Koh JE. Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowledge-Based Systems 2015;88:85–96.
• [15] Song Y, Zhang J. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine. J Neurosci Methods 2016;257:45–54.
• [16] Mahajan R, Morshed BI. Unsupervised eye blink artifact denoising of EEG data with modified multiscale sample entropy, kurtosis, and Wavelet-ICA. IEEE J Biomed Health Informat 2015;19(1):158–65.
• [17] Sucic V, Saulig N, Boashash B. Analysis of local time- frequency entropy features for nonstationary signal components time supports detection. Digital Signal Process 2014;34:56–66.
• [18] Nguyen HD, Wilkins BA, Cheng Q, Benjamin BA. An online sleep apnea detection method based on recurrence quantification analysis. IEEE J Biomed Health Informat 2014;18(4):1285–93.
• [19] Mosdorf R, Górski G. Identification of two-phase flow patterns in minichannel based on RQA and PCA analysis. Int J Heat Mass Transfer 2016;96:64–74.
• [20] Ngamga EJ, Bialonski S, Marwan N, Kurths J, Geier C, Lehnertz K. Evaluation of selected recurrence measures in discriminating pre-ictal and inter-ictal periods from epileptic EEG data. Phys Lett A 2016;380(16):1419–25.
• [21] Nigam VP, Graupe D. A neural network based detection of epilepsy. Neurol Res 2004;26(1):55–60.
• [22] Srinivasan V, Eswaran C, Sriraam AN. Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Systems 2005;29(6):647–60.
• [23] Sezer E, Işik H, Saracoğlu E. Employment and comparison of different artificial neural networks for epilepsy diagnosis from EEG signals. J Med Systems 2012;36(1):347–62.
• [24] Musselman M, Djurdjanovic D. Time–frequency distributions in the classification of epilepsy from EEG signals. Expert Systems Appl 2012;39(13):11413–22.
• [25] Tzallas AT, Tsipouras MG, Fotiadis DI. Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Trans Inform Technol Biomed 2009;13(5):703–10.
• [26] Güler I, Übeyli ED. Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J Neurosci Methods 2005;148(2):113–21.
• [27] Übeyli ED. Automatic detection of electroencephalographic changes using adaptive neuro-fuzzy inference system employing Lyapunov exponents. Expert Systems Appl 2009;36(5):9031–8.
• [28] Shayegh F, Sadri S, Amirfattahi R, Ansari-Asl K. A model-based method for computation of correlation dimension. Lyapunov exponents and synchronization from depth-EEG signals. Computer Methods Programs Biomed 2014;113 (1):323–37.
• [29] Subasi A. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems Appl 2007;32(4):1084–93.
• [30] Kumar Y, Dewal ML, Anand RS. Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 2014;133:271–9.
• [31] Yildiz A, Akin M, Poyraz M, Kirbas G. Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction. Expert Systems Appl 2009;36(4):7390–9.
• [32] Liu Y, Zhou W, Yuan Q, Chen S. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Trans Neural Systems Rehabil Eng 2012;20(6):749–55.
• [33] Geng D, Zhou W, Zhang Y, Geng S. Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG. Biocybernet Biomed Eng 2016;36(2):375–84.
• [34] Li S, Zhou W, Yuan Q, Geng S, Cai D. Feature extraction and recognition of ictal EEG using EMD and SVM. Computers Biol Med 2013;43(7):807–16.
• [35] Bajaj V, Pachori RB. Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomed Eng Lett 2013;3(1):17–21.
• [36] Alam SS, Bhuiyan MIH. Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE J Biomed Health Inform 2013;17(2):312–8.
• [37] Djemili R, Bourouba H, Korba MA. Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals. Biocybernet Biomed Eng 2016;36(1):285–91.
• [38] Riaz F, Hassan A, Rehman S, Niazi IK, Dremstrup K. EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans Neural Systems Rehabil Eng 2016;24(1):28–35.
• [39] 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 Systems 2012;22(06):1250027.
• [40] Zeng K, Chen D, Ouyang G, Wang L, Liu X, Li X. An EEMD-ICA approach to enhancing artifact rejection for noisy multivariate neural data. IEEE Trans Neural Syst Rehabil Eng 2016;24(6):630–8.
• [41] Fu K, Qu J, Chai Y, Dong Y. Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomed Signal Process Control 2014;13:15–22.
• [42] Sharma R, Pachori RB. Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Systems Appl 2015;42 (3):1106–17.
• [43] Pachori RB, Avinash P, Shashank K, Sharma R, Acharya UR. Application of empirical mode decomposition for analysis of normal and diabetic RR-interval signals. Expert Systems Appl 2015;42(9):4567–81.
• [44] Sareen S, Sood SK, Gupta SK. A cloud-based seizure alert system for epileptic patients that uses higher-order statistics. Comput Sci Eng 2016;18(5):56–67.
• [45] Martis RJ, Acharya UR, Prasad H, Chua CK, Lim CM, Suri JS. Application of higher order statistics for atrial arrhythmia classification. Biomed Signal Process Control 2013;8(6):888– 900.
• [46] Acharya UR, Sudarshan VK, Koh JE, Martis RJ, Tan JH, Oh SL, et al. Application of higher-order spectra for the characterization of Coronary artery disease using electrocardiogram signals. Biomed Signal Process Control 2017;31:31–43.
• [47] Kevric J, Subasi A. Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. Biomed Signal Process Control 2017;31:398–406.
• [48] Brodu N, Lotte F, Lécuyer A. Exploring two novel features for EEG-based brain–computer interfaces: multifractal cumulants and predictive complexity. Neurocomputing 2012;79:87–94.
• [49] Hassan AR, Bhuiyan MI. Automatic sleep scoring using statistical features in the EMD domain and ensemble methods. Biocybernet Biomed Eng 2016;36(1):248–55.
• [50] Noshadi S, Abootalebi V, Sadeghi MT, Shahvazian MS. Selection of an efficient feature space for EEG-based mental task discrimination. Biocybernet Biomed Eng 2014;34 (3):159–68.
• [51] Lozano M, Fiz JA, Jané R. Performance evaluation of the Hilbert–Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization. Signal Process 2016;120:99–116.
• [52] EEG time series download. Available on: http://epileptologie-bonn.de.cms.front_content.php? idcat=193&lang=3 accessed on 10 June 2015.
• [53] 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(6):061907.
• [54] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Royal Society of London A: Mathematical, Physical and Engineering Sciences (vol. 454, No. 1971, pp. 903–995). The Royal Society; 1998.
• [55] Oweis RJ, Abdulhay EW. Seizure classification in EEG signals utilizing Hilbert–Huang transform. Biomed Eng Online 2011;10(1):38–52.
• [56] Vogl TP, Mangis JK, Rigler AK, Zink WT, Alkon DL. Accelerating the convergence of the back-propagation method. Biol Cybernet 1988;59(4–5):257–63.
• [57] Møller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 1993;6(4):525–33.
• [58] Bhattacharyya S, Basu D, Konar A, Tibarewala DN. Interval type-2 fuzzy logic based multiclass ANFIS algorithm for real-time EEG based movement control of a robot arm. Robot Autonom Syst 2015;68:104–15.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).