Epileptic seizures happen because of neuronal disorder that produces an unusual pattern of brain signals. Automatic seizure detection has proved to be a challenging task, for both long terms monitoring as well as epilepsy diagnosis. In this work, the proposed discrete wavelet transform (DWT) based singular value decomposition fuzzy k-nearest neighbor classifier (SVD-FkNN) technique, is one of the most effective methods in supervised learning, which provides good accuracy with fast learning speed in comparison to several other conventional techniques. In this work, both feature extraction and classification of EEG signals have been done for epilepsy detection of the human brain using the Bonn University dataset. The proposed method is based on the multi-scale eigenspace analysis of the matrices, generated from the sub-band signals of all EEG channels using DWT by SVD at a substantial scale and are classified using extracted singular value features and FkNN classifier with different ‘k’ values to obtain better accuracy. The proposed DWT based SVD-FkNN technique has been applied for the first time on the EEG signal for epilepsy detection (using five-class classifications). The experimental results of the proposed method give an overall accuracy of 100% for two and three class classification and 93.33% ( 0.001) for five class classification.
CorsiniJShokerLSaneiSAlarconG. Epileptic seizure predictability from scalp EEG incorporating constrained blind source separation. IEEE Transactions on Biomedical Engineering2006; 53(5): 790-799.
4.
SornmoLLagunaP. Bioelectrical signal processing in cardiac and neurological applications. Academic Press, 2005.
5.
ClemensBSzigetiGBartaZ. EEG frequency profiles of idiopathic generalised epilepsy syndromes. Epilepsy Research2000; 42(2-3): 105-115.
6.
KannathalNAcharyaURLimCM and SadasivanPK. Characterization of EEG-A comparative study. Computer methods and Programs in Biomedicine2005; 80(1): 17-23.
7.
KellawayP. An orderly approach to visual analysis: characteristics of the normal EEG of adults and children. In: DalyDPedleyT, eds. Current practice of clinical electroencephalography, 2 ed. New York, NY: Raven Press 1990; pp. 139-199.
GotmanJ. A few thoughts on “What is a seizure”. Epilepsy Behavior2011; 22: S2-S3.
10.
VidyaratneLSIftekharuddinKM. Real-time epileptic seizure detection using EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering2017; 25(11): 2146-2156.
11.
SimonsSAbasoloDEscuderoJ. Classification of Alzheimer’s disease from quadratic sample entropy of electroencephalogram. Healthcare Technology Letters2015; 2(3): 70-73.
12.
ZhouJWuXMZengWJ. Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine. Journal of Clinical Monitoring and Computing2015; 29(6): 767-772.
13.
PetrantonakisPCHadjileontiadisLJ. Emotion recognition from EEG using higher order crossings. IEEE Transactions on Information Technology in Biomedicine2010; 14(2): 186-197.
14.
KellerJMGrayMRGivensJA. A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics1985; 15(4): 580-585.
15.
KumariRSSJoseJP. Seizure detection in EEG using bi-orthogonal wavelet and fuzzy KNN classifier. Human Physiology2011; 41: 5766-5770.
16.
ChenHLHuangCCYuXGXuXSunXWangGWangSJ. An efficient diagnosis system for detection of parkinsons disease using fuzzy k-nearest neighbour approach. Expert Systems with Application2013; 40(1): 263-271.
17.
ShakhnarovishGDarrellTIndykP. Nearest-neighbor methods in learning and vision. The MIT press 2005.
18.
RossoOMartinMFigliolaAKellerKPlastinoA. EEG analysis using wavelet-based information tools. Journal of Neuroscience Methods2006; 153(2): 163-182.
19.
LiuYZhouWYuanQChenS. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering2012; 20(6): 749-755.
20.
ZandiASJavidanMDumontGATafreshiR. Automated real-time epileptic seizure detection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEE Transactions on Biomedical Engineering2010; 57(7): 1639-1651.
21.
NiknazarMMousaviSRVahdatBVSayyahM. A new framework based on recurrence quantification analysis for epileptic seizure detection. IEEE Journal of Biomedical and Health Informatics2013; 17(3): 572-578.
22.
AlamSMSBhuiyanMIH. Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE Journal of Biomedical and Health Informatics2013; 17(2): 312-318.
23.
SamieeKKovacsPGabboujM. Epileptic seizure classification of EEG time-series using rational discrete short-time fourier transform. IEEE Transactions on Biomedical Engineering2015; 62(2): 541-552.
24.
UthayakumarREaswaramoorthyD. Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform. Fractals2013; 21(2): 1350-011.
25.
SharmilaAGeetanjaliP. DWT based detection of epileptic seizure from EEG signals using naive bayes and k-NN classifiers. Digital Object Identifier2016; 7716-7727.
26.
GajicDDjurovicZGennaroSDGustafssonF. Classification of EEG signals for detection of epileptic seizures based on wavelets and statistical pattern recognition. Biomedical Engineering: Applications, Basis and Communications2014; 26(2): 1-13.
27.
SatapathySKDehuriSJagadevAK. EEG signal classification using PSO trained RBF neural network for epilepsy classification. Informatics in Medicine Unlocked2017; 6: 1-11.
28.
GuoLRiveroDDoradoJMunteanuCRPazosA. Automatic feature extraction using genetic programming: An application to epileptic EEG classification. Expert Systems with Applications2011; 38(8): 10425-10436.
29.
OrhanUHekimMOzerM. EEG signals classification using the K-means clustering and a multilayer perceptron neural network model. Expert Systems with Applications2011; 38(10): 13475-13481.
30.
TiwariAKPachoriRBKanhangadVPanigrahiBK. Automated diagnosis of epilepsy using key-point-based local binary pattern of EEG signals. IEEE Journal of Biomedical and Health Informatics2017; 21(4): 888-896.
31.
JiangYWuDDengZQianPWangJWangGChungFLChoiKSWangS. Seizure classification from EEG signals using transfer learning, semi-supervised learning and TSK fuzzy system. IEEE Transactions on Neural Systems and Rehabilitation Engineering2017; 25(12): 2270-2284.
32.
FuKQuJChaiYDongY. Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM. Biomedical Signal Processing and Control2014; 13: 15-22.
33.
TzallasATTsipourasMGFotiadisDI. Epileptic seizure detection in EEGs using time-frequency analysis. IEEE Transactions on Information Technology in Biomedicine2009; 13(5): 703-710.
34.
PekerMSenmBDelenD. A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE Journal of Biomedical and Health Informatics2016; 20(1): 108-118.
35.
KayaYUyarMTekinRYldrmS. 1D-local binary pattern-based feature extraction for classification of epileptic EEG signals. Applied Mathematics and Computation2014; 243: 209-219.
36.
PachoriRBSharmaRPatidarS. Classification of normal and epileptic seizure EEG signals based on empirical mode decomposition. Complex System Modelling and Control Through Intelligent Soft Computations Studies in Fuzziness and Soft Computing2015; 367-388.
37.
SubasiAGursoyMI. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Systems with Applications2010; 37(12): 8659-8666.
38.
ChenG. Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features. Expert Systems with Applications2014; 41(5): 2391-2394.
39.
GuoLRiveroDDoradoJRabunaJRPazosA. Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. Journal of Neuroscience Methods2010; 191: 101-109.
40.
WangDMiaoDQXieC. Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection. Expert Systems with Applications2011; 38(11): 14314-14320.
41.
JoshiVPachoriVRBVijeshA. Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomedical Signal Processing and Control2014; 9: 1-5.
42.
AndrzejakRGLehnertzKMormannFRiekeCDavidPElgerCE. 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, Stat. Phys. Plasmas Fluids Relat. Interdiscip. Top.2001; 64(6): 061907-1-8.
43.
MallatSG. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence1989; 11(7): 674-693.
44.
AdeliHZhouZDadmehrN. Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods2003; 123(1): 69-87.
45.
DaubechiesI. Ten Lectures on Wavelets. Philadelphia, PA: Society for Industrial and Applied Mathematics 1992.
46.
KhanYUGotmanJ. Wavelet based automatic seizure detection in intra cerebral electroencephalogram. Clinical Neurophysiology2003; 114(5): 898-908.
47.
SaabMEGotmanJ. A system to detect the onset of epileptic seizures in scalp EEG. Clinical Neurophysiology2005; 116(2): 427-442.
48.
SupriyaSSiulySWangHCaoJJhangY. Weighted visibility graph with complex network features in the detection of epilepsy. IEEE Accesses2016; 4: 6554-6566.
49.
DurkaPJ. From wavelets to adaptive approximations: Time-frequency parameterization of EEG. Biomedical Engineering Online2003; 2(1): 1-30.
50.
AdeliHDastidarSGDadmehrN. A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering2007; 54(2): 205-211.
51.
BaghaSTripathyRKNandaPPreetamCDasDP. Understanding perception of active noise control system through multichannel EEG analysis. Healthcare Technology Letter2018; 5(3): 101-106.
52.
SharmaLNTripathyRKDandapatS. Multi-scale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Transactions on Biomedical Engineering2015; 62(7): 1827-1837.
53.
SubasiA. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications2007; 32(4): 1084-1093.
54.
CoverTHartP. Nearest neighbor pattern classification. IEEE Transactions on Information Theory1967; 13(1): 21-27.
55.
SokolovaMLapalmeG. A systematic analysis of performance measures for classification tasks. Information Processing & Management2009; 45(4): 427-437.
56.
KellerJMGrayMRGivensJA. A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics1985; 15(4): 580-585.
57.
ShangWHuangHZhuHLinYQuYWangZ. A novel feature selection algorithm for text categorization. Expert Systems with Applications2007; 33: 1-5.
58.
KumariRSSJoseJP. Seizure detection in EEG using bi-orthogonal wavelet and fuzzy KNN classifier. Human Physiology2011; 41: 5766-5770.
59.
DerracJGarcíaSHerreraF. Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects. Information Sciences2014; 260: 98-119.
60.
ZhangTChenWLiM. Generalized Stockwell transform and SVD-based epileptic seizure detection in EEG using random forest. Biocybernetics & Biomedical Engineering2018; 38: 519-534.
61.
LiDXieQJinQHirasawaK. A sequential method using multiplicative extreme learning machine for epileptic seizure detection. Neurocomputing2016; 214: 692-707.
62.
UbeyliEDGulerI. Features extracted by eigenvector methods for detecting variability of EEG signals. Pattern Recognition Letters2007; 28(5): 592-603.
63.
DasABBhuiyanMIHAlamSMS. Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. Signal, Image and Video Processing2016; 10(2): 259-266.
64.
GulerNFUbeyliEDGulerI. Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Systems with Applications2005; 29(3): 506-514.
65.
LiMChenWZhangT. Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomedical Signal Processing and Control2017; 31: 357-365.
