Detection and Classification of Genetically Transfered Idiopathic Partial Epilepsy to Child:a Four Rule ANFIS based SWT-EBFO Approach

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Background:-Hereditary qualities have an influence in numerous sorts of epilepsy. In the event that a parent has idiopathic epilepsy, there is around 5% to 8% chance that the youngster upto 8 years will likewise have epilepsy called as idiopathic partial epilepsy (IPE).

    Methods:-This exploration work breaks down the epilepsy issue exchange hereditarily by coordinating the best properties of Enhanced Bacterial Foraging Optimization (EBFO) and Least-mean-square (LMS) algorithm with four rule Adaptive Neuro-fuzzy Inference System (ANFIS) Network. Stockwell Transform (SWT) strategy searched for the extraction of decomposed signal. In this work, quantitative tests and statistical tests are performed by utilizing SWT-ANFIS-EBFO and SWT-ANFIS-LMS strategies.

    Results:-Our proposed statistical results(Accuracy (98.30%), sensitivity (98.23%), specificity (99.53%)   and Matthew’s correlation coefficient (97.08%),G-mean (98.88%) and average detection ratio (98.93%)) are calculated withthe network SWT-ANFIS-LMS. Proposed statistical results (accuracy (99.49%), sensitivity (98.78%), specificity (98.56%), and Matthew’s correlation coefficient (97.907%), G-mean (99.172) and average detection ratio (99.174%)) are calculated SWT-ANFIS-EBFO beats. The calculated quantitative test results for network SWT-ANFIS-LMS are (SNR 18.42±0.18, RE 0.11±0.02, CC 61±0.012, MFRE 0.41±0.02) and for network SWT-ANFIS-MFRE are (SNR 18.42±0.18, RE 0.11±0.02, CC 61±0.012, MFRE 0.41±0.02).

    Conclusion:-In this paper we endeavor to investigate the best capability of SWT based ANFISnetwork trained with EBFO and LMS algorithms for classification of IPE EEG signals.Calculated statisticaland quantative test results of the proposed method outperforms as compared to existing methods. It will end up being a significant trial device in clinical application and advantageous application towards IPE influenced patients.

     


  • Keywords


    IPE, EBFO, LMS, SWT, ANFIS.

  • References


      [1] Fisher R, van Emde Boas W, Blume W, Elger C, Genton P, Lee P, Engel J (2005). "Epileptic seizures and epilepsy: definitions proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE)". Epilepsia. 46 (4): 470–2.

      [2] Fisher RS, Acevedo C, Arzimanoglou A, Bogacz A, Cross JH, Elger CE, Engel J, Forsgren L, French JA, Glynn M, Hesdorffer DC, Lee BI, Mathern GW, Moshé SL, Perucca E, Scheffer IE, Tomson T, Watanabe M, Wiebe S (Apr 2014). "ILAE Official Report: A practical clinical definition of epilepsy.". Epilepsia. 55 (4): 475–82.

      [3] Adeli, H., Z. & Dad mehr.N. (2003). Analysis of EEG records in an epileptic Patient using wavelet transform, Journal of Neuroscience methods,123,69-87.

      [4] Subasi,A.(2007).EEG Signal classification using wavelet feature extraction and a mixture of export model. Export systems with Applications, 32, 1048-1093.

      [5] Gabor AJ, Seyal M. Automated interictal EEG spike detection using artificial neural networks. ElectroencephalogramClinNeurophysiology 1992; 83(5):271–80.

      [6] Jando G, Siegel RM, Horvath Z, Buzsaki G. Pattern recognition of the electroencephalogram by artificial neural networks. ElectroencephalogrClinNeurophysiol 1993; 86(2):100–9.

      [7] Webber WRS, Litt B, Wilson K, Lesser RP. Practical detection of epileptiform discharges (Eds) in the EEG using an artificial neural network: a comparison of raw and parameterized EEG data. Electroencephalogram ClinNeurophysiol 1994; 91(3):194–204.

      [8] Pradhan N, Sadasivan PK, Arunodaya GR. Detection of seizure activity in EEG by an artificial neural network: a preliminary study. Comput Biomed Res 1996; 29(4):303–13.

      [9] Varsta M, Heikkonen J, Del J, Millan R. Epileptic activity detection in EEG with neural networks. In: Proc of 3rd intconfeng applications of neural networks. P. 179–86.

      [10] Gabor AJ. Seizure detection using a self-organizing neural network: validation and comparison with other detection strategies. Electroencephalogram Clin Neurophysiology 1998; 107(1):27–32.

      [11] Walczack S, Nowack WJ. An artificial neural network approach to diagnosing epilepsy using lateralized bursts of theta EEGs. J Med Syst 2001; 25(1):9–20.

      [12] Nigam VP, Graupe D. A neural-network-based detection of epilepsy. Neurol Res 2004; 26(1):55–60Woo, S. L. C. Personal Communication. Houston, Tex. 1/11/1983.

      [13] Majumdar K. Human scalp EEG processing: various soft computing approaches. Appl Soft Comput 2011;11(8):4433–47.DiLella, A. G., Huang, W.-M., Woo, S. L. C. Screening for phenylketonuria mutations by DNA amplification with the polymerase chain reaction.Lancet 331: 497-499, 1988. Note: Originally Volume I.

      [14] Mishra BSP, Dehuri S, Wang GN. A state-of-the-art review of artificial bee colony in the optimization of single and multiple criteria. Int J ApplMetaheuristComput 2013;4(1):32–49.

      [15] Robert C, Gaudy JF, Limoge A. Electroencephalogram processing using neural networks. ClinNeurophysiol 2000;113(5):694–701.

      [16] Aslan K, Bozdemir H, Sahin C, Ogulata SN, Erol R. A radial basis function neural network model for classification of epilepsy using EEG signals. J Med Syst 2008;32(5):403–8.Gross D, Grassino A,. Ross WRD, Macklem PT. Electromyogram pattern of diaphragmatic fatigue. Applied Physiology 1979;46:1–7.

      [17] U. Orhan, M. Hekim, M. Ozer, EEG signals classification using the K-means clustering and a multilayer perceptron neural network model, Expert Syst. Appl. 38 (10) (2011) 13475–13481.

      [18] A.T. Tzallas, M.G. Tsipouras, D.I. Fotiadis, Automatic seizure detection based on time-frequency analysis and artificial neural networks, Comput. Intell. Neurosci. 2007 (4) (2007) 80510.

      [19] S. Ghosh-Dastidar, H. Adeli, N. Dadmehr, Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection, IEEE Trans. Biomed. Eng. 54 (9) (2007) 1545–1551.

      [20] V. Srinivasan, C. Eswaran, N. Sriraam, Artificial neural network based epileptic detection using time-domain and frequency domain features, J. Med. Syst. 29 (6) (2005) 647–66

      [21] A. B. Das, M. I. H. Bhuiyan, Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMDDWT domain, Biomedical Signal Processing and Control 29 (2016) 11–21.23Page 25 of 32Accepted Manuscript

      [22] R. Sharma, R. B. Pachori, U. R. Acharya, An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures, Entropy 17 (2015) 5218–5240.

      [23] M. Sharma, A. Dhere, R. B. Pachori, U. R. Acharya, An automatic detection of focal EEG signals using new class of time-frequency localized orthogonal wavelet filter banks, Knowledge-Based Systems 118 (2017) 217–227.

      [24] D. Bhati, M. Sharma, R. B. Pachori, V. M. Gadre, Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification, Digital Signal Processing 62 (2017) 259–273.

      [25] H. Adeli, S. Ghosh-Dastidar, N. Dadmehr, A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy, IEEE Transactions on Biomedical Engineering 54 (2007) 205–211.

      [26] S. Ghosh-Dastidar, H. Adeli, N. Dadmehr, Mixed-band wavelet-chaosneural network methodology for epilepsy and epileptic seizure detection, IEEE Transactions on Biomedical Engineering 54 (2007) 1545–1551

      [27] Stockwell RG. A basis for efficient representation of the S-transform. Digital Signal Process 2007; 17(1):371–93.

      [28] Stockwell RG, Mansinha L, Lowe RP. Localization of the complex spectrum: the S transform. IEEE Trans Signal Process 1996; 44 (4):998–1001.

      [29] K.M. Passino, Biomimicry of bacterial foraging for distributed optimization and control, IEEEControl Systems Magazine 22 (3) (2002) 52–67.

      [30] Tizhoosh, H.R. Opposition-based learning: A new scheme for machine intelligence. InProceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet, Vienna, Austria, 28–30 November 2005; pp. 695–701.

      [31] P.S.R. Diniz, Adaptive Filtering: Algorithms and Practical Implementation, 3rd edition, Springer, New York, NY, USA, 2008.

      [32] A.D. Poularikas, Z.M. Ramadan, Adaptive Filtering primer with MATLAB, CRC, London, New York, 2006

      [33] Jang, J.-S. R., "ANFIS: Adaptive-Network-based Fuzzy Inference Systems," IEEE Transactionson Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, May 1993.

      [34] U.R. Acharya, F. Molinari, S.V. Sree, S. Chattopadhyay, K.H. Ng, J.S. Suri, Automated diagnosis of epileptic EEG using entropies, Biomed. Signal Process. Control 7 (4) (2012) 401–408.

      [35] Mingyang Li, Wanzhong Chen, Tao Zhang, Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble, Biomedical Signal Processing and Control 31 (2017) 357–365.

      [36] ShivnarayanPatidar , Ram Bilas Pachori , AbhayUpadhyay , U. Rajendra Acharya, An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism, Applied Soft Computing xxx (2016) xxx–xxx.

      [37] Ram Bilas Pachori , ShivnarayanPatidar, Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions, comput e r me thods and p rograms in b iomedi c ine 1 1 3 ( 2 0 1 4 ) 494–502.

      [38] S. Altunay, Z. Telatar, O. Erogul, Epileptic EEG detection using the linear prediction error energy, Expert System with Applications 37 (8) (2010) 5661–5665, August.

      [39] V. Joshi, R.B. Pachori, A. Vijesh, Classification of ictal and seizure-free EEG signals using fractional linear prediction, Biomedical Signal Processing and Control 9 (2014) 1–5, January.

      [40] Aiyu Yan , Weidong Zhou , Qi Yuan , Shasha Yuan , Qi Wu , Xiuhe Zhao , Jiwen Wang, Automatic seizure detection using Stockwell transform and boosting algorithm for long-term EEG, Epilepsy & Behavior 45 (2015) 8–14.

      [41] Liu YX, Zhou WD, Yuan Q, Chen SS. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Trans Neural SystRehabilEng 2014; 20(6):749–55.

      [42] T. Sunil Kumar, VivekKanhangad∗, Ram Bilas Pachori, Classification of seizure and seizure-free EEG signals using local binary patterns, Biomedical Signal Processing and Control 15 (2015) 33–40.

      [43] Sang-Hong Lee , Joon S. Limb, Jae-Kwon Kimc , JunggiYangb, Youngho Lee, Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance, comput e r me thods and programs in biomedicine 1 1 6 ( 2 0 1 4 ) 10–25.

      [44] S. Chandaka, A. Chatterjee, S. Munshi, Cross-correlation aided support vector machine classifier for classification of EEG signals, Expert Syst. Appl. 36 (2009) 1329–1336.

      [45] G. Ling, R. Daniel, A.S. Jose, P. Alejandro, Classification of EEG Signals Using Relative Wavelet Energy and Artificial Neural Networks, GEC, 2009, June, pp. 177–183.

      [46] A. Subasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Syst. Appl. 32 (2007) 1084–1093.

      [47] Varun Joshi, Ram Bilas Pachori , Antony Vijesh, Classification of ictal and seizure-free EEG signals using fractional linear prediction, Biomedical Signal Processing and Control 9 (2014) 1–5.

      [48] AbdulhamitSubasi, EEG signal classification using wavelet feature extraction and a mixture of expert model, Expert Systems with Applications 32 (2007) 1084–1093.

      [49] ShivnarayanPatidar, TrilochanPanigrahi, Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals, Biomedical Signal Processing and Control 34 (2017) 74–80.

      [50] P. Swami, T.K. Gandhi, B.K. Panigrahi, M. Tripathi, S. Anand, A novel robust diagnostic model to detect seizures in electroencephalography, Expert Syst. Appl. 56 (2016) 116–130.

      [51] R.B. Pachori, S. Patidar, Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions, Comput. Methods Programs Biomed. 113 (2) (2014) 494–502.


 

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Article ID: 19368
 
DOI: 10.14419/ijet.v7i3.34.19368




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