Scattering transform-based features for the automatic seizure detection

• Autorzy: Jiang Yun , Chen Wanzhong , You Yang

Identyfikatory

DOI

10.1016/j.bbe.2019.11.002

• Języki publikacji: EN

Abstrakty

EN

Developing the automatic detection system is of great clinical significance for assisting neurologists to detect epilepsy using electroencephalogram (EEG) signals. In this research, we explore the ability of a newly-developed algorithm named scattering transform in seizure detection. The preprocessed signal is initially decomposed into scattering coefficients with various orders and scales employing scattering transform. Fuzzy entropy (FuzzyEn) and Log energy entropy (LogEn) of the sub-band coefficients are obtained to characterize the epileptic seizure signals. Then the joint features are fed into five classifiers including support vector machine (SVM), least squares-support vector machine (LS-SVM), genetic algorithm-support vector machine (GA-SVM), extreme learning machine (ELM) and probabilistic neural network (PNN) for the verification of the effectiveness of the proposed scheme. Finally, we not only compare the classification results and the time efficiency derived from different classifiers, but also explore the discrimination performance of the proposed methodology based on ten different classification tasks with great clinical significance. The prominent classification accuracy (ACC) of 99.87 %, 99.59 %, 99.58 %, 99.56 % and 99.80 % are achieved using the above five classifiers respectively. The average ACC and Matthews correlation coefficient (MCC) of 99.75 % and 0.99 are also yielded based on all tasks. Furthermore, the result of Kruskal-Wallis Test for the verification of statistical significance confirms the reliability of the proposal. The comparison with the latest state-of-the-art techniques indicates the superior performance of the proposal. A tradeoff between classification accuracy and time complexity of the proposed approach is accomplished in our work and the possibility for clinical application is also demonstrated.

Słowa kluczowe

EN

EEG signal; scattering transform; fuzzy entropy; Log energy entropy; seizure detection

PL

sygnał EEG; entropia rozmyta; wykrywanie napadu

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 77--89
• Opis fizyczny: Bibliogr. 48 poz., rys., tab., wykr.

Twórcy

autor

Jiang Yun

• College of Communication Engineering, Jilin University, Changchun 130025, China

autor

Chen Wanzhong

• College of Communication Engineering, Jilin University, Changchun 130025, China

autor

You Yang

• College of Communication Engineering, Jilin University, Changchun 130025, China

Bibliografia

• [1] Kumar Y, Dewal ML, Anand RS. Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine. Neurocomputing 2014;133:271–9.
• [2] 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.
• [3] World Health Organization, Epilepsy. https://www.who.int/ en/news-room/fact-sheets/detail/epilepsy.
• [4] Peker M, Sen B, Delen D. A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE J Biomed Health Inform 2016;20(1):108–18.
• [5] 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.
• [6] Geng D, Zhou W, Zhang Y, Geng S. Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG. Biocybern Biomed Eng 2016;36 (2):375–84.
• [7] Martis RJ, Tan JH, Chua CK, Loon TC, Yeo SWJ, Tong L. Epileptic EEG classification using nonlinear parameters on different frequency bands. J Mech Med Biol 2015;15 (3):1550040.
• [8] 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.
• [9] Sharmila A, Geethanjali P. Effect of filtering with time domain features for the detection of epileptic seizure from EEG signals. J Med Eng Technol 2018;42(3):217–27.
• [10] Hussain L. Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach. Cogn Neurodyn 2018;12(3):271–94.
• [11] Chatterjee S, Choudhury N, Bose R. Detection of epileptic seizure and seizure-free EEG signals employing generalised S-transform. Iet Sci Meas Technol 2017;11(7):847–55.
• [12] Arunkumar N, Ramkumar K, Venkatraman V, Abdulhay Enas, Lawrence-Fernandes Steven, Kadry Seifedine, et al. Classification of focal and non focal EEG using entropies. Pattern Recognit Lett 2017;94:112–7.
• [13] Gupta V, Pachori RB. Epileptic seizure identification using entropy of FBSE based EEG rhythms. Biomed Signal Process Control 2019;53:101569.
• [14] 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.
• [15] Sharmila A, Aman Raj S, Shashank P, Mahalakshmi P. Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case study. J Med Eng Technol 2018;42(1):1–8.
• [16] Li M, Chen W, Zhang T. FuzzyEn-based features in FrFT-WPT domain for epileptic seizure detection. Neural Comput Appl 2018.
• [17] Biju KS, Hakkim HA, Jibukumar MG. Ictal EEG classification based on amplitude and frequency contours of IMFs. Biocybern Biomed Eng 2017;37(1):172–83.
• [18] Cura OK, Atli SK, Sadighzadeh R, Akan A. Classification of epileptic EEG data by using ensemble empirical mode decomposition. 2018 26th Signal Processing and Communications Applications Conference (SIU). 2018;1–4.
• [19] Sharma M, Pachori RB. A novel approach to detect epileptic seizures using a combination of Tunable-q wavelet transform and fractal dimension. J Mech Med Biol 2017;17 (7):1740003.
• [20] Liu Y, Zhou W, Yuan Q, Chen S. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. Ieee Trans Neural Syst Rehabil Eng 2012;20 (6):49–55.
• [21] Raghu S, Sriraam N, Temel Y, Rao SV, Hegde AS, Kubben PL. Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier. Comput Biol Med 2019;110:127–43.
• [22] Djemili R, Bourouba H, Amara Korba MC. Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals. Biocybern Biomed Eng 2016;36(1):285–91.
• [23] Alam SMS, Bhuiyan MIH. Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE J Biomed Health Inform 2013;17(2):312–28.
• [24] Wu Z, Huang NE. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 2009;1(1):1–41.
• [25] Das K, Mourya GK. Classification of EEG signals in the improved complete ensemble EMD domain. 2018 2nd international conference on power, energy and environment: towards smart technology (ICEPE). 2018;1–9.
• [26] Zheng J, Pan H, Yang S, Cheng J. Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis. Signal Process 2017;130:305–14.
• [27] Bruna J, Mallat S. Invariant scattering convolution networks. IEEE Trans Pattern Anal Mach Intell 2013;35 (8):1872–86.
• [28] Leonarduzzi R, Liu H, Wang Y. Scattering transform and sparse linear classifiers for art authentication. Signal Processing 2018;150:11–9.
• [29] Ma S, Cheng B, Shang Z, Liu G. Scattering transform and LSPTSVM based fault diagnosis of rotating machinery. Mech Syst Signal Process 2018;104:155–70.
• [30] Souli S, Lachiri Z. Audio sounds classification using scattering features and support vectors machines for medical surveillance. Appl Acoust 2018;130:270–82.
• [31] EEG Time Series Data (Department of Epileptology University of Bonn, Germany). http://epileptologie-bonn.de/ cms/front_content.php?idcat=193&lang=3&changelang=3. Accessed Sept 2019.
• [32] 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. Physical Review E - Statistical Nonlinear and Soft Matter Physics 2001;64(6 I):061907–8.
• [33] Xiang J, Li C, Li H, Cao R, Wang B, Han X, et al. The detection of epileptic seizure signals based on fuzzy entropy. J Neurosci Methods 2015;243:18–25.
• [34] Aydin S, Saraoglu H, Kara S. Log energy entropy-based EEG classification with multilayer neural networks in seizure. Ann Biomed Eng 2009;37(12):2626–30.
• [35] Cherkassky V. The nature of statistical learning theory. IEEE Trans Neural Netw 1997;8(6):1564.
• [36] Temko A, Thomas E, Marnane W, Lightbody G, Boylan G. EEG-based neonatal seizure detection with support vector machines. Clin Neurophysiol 2011;122(3):464–73.
• [37] Suykens JAK, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett 1999;9(3):293–300.
• [38] Sharma M, Pachori RB, Rajendra Acharya U. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit Lett 2017;94:172–9.
• [39] Huang G, Zhu Q, Siew C. Extreme learning machine: theory and applications. Neurocomputing 2006;70(1):489–501.
• [40] Specht D. Probabilistic neural networks. Neural Netw 1990;3 (1):109–18.
• [41] Tiwari AK, Pachori RB, Kanhangad V, Panigrahi BK. Automated diagnosis of epilepsy using key-point-Based local binary pattern of EEG signals. IEEE J Biomed Health Inform 2017;21(4):888–96.
• [42] Das AB, Bhuiyan MIH, Alam SMS. Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. Signal Image Video Process 2016;10(2):259–66.
• [43] Siuly S, Alcin OF, Bajaj V, Sengur A, Zhang Y. Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure. Iet Sci Meas Technol 2019;13 (1):35–41.
• [44] 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 Syst Rehabil Eng 2016;24(1):28–35.
• [45] Mei Z, Mei Z, Zhao X, Zhao X, Chen H, Chen H, et al. A distributed descriptor characterizing structural irregularity of EEG time series for epileptic seizure detection. Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS; 2018;3386–9.
• [46] Deng Z, Xu P, Xie L, Choi K, Wang S. Transductive joint-knowledge-Transfer TSK FS for recognition of epileptic EEG signals. Ieee Trans Neural Syst Rehabil Eng 2018;26 (8):1481–94.
• [47] Hassan AR, Siuly S, Zhang Y. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput Methods Programs Biomed 2016;137:247–59.
• [48] Hassan AR, Subasi A. Automatic identification of epileptic seizures from EEG signals using linear programming boosting. Comput Methods Programs Biomed 2016;136:65–77.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).