Abstract
Epilepsy can well be stated as a disorder of the central nervous systems (CNS) that brought about recurring seizures owing to chronic abnormal blasts of electrical discharge on the brain. Knowing if an individual is having a seizure and diagnosing the seizure type or epilepsy syndrome could be hard. Many methods were developed to recognize this disease. But the existing techniques for detection of epilepsy are not satisfied with accuracy, and cannot identify the diseases effectively. To trounce these drawbacks, this paper proposes an approach for the recognition of Epilepsy as of the electroencephalography (EEG) signals. This is implemented as follows. Primarily, the Kalman filter (KF)is utilized for pre-processing to eradicate the impulse noise present in the EEG signals. This filtered signal is then decomposed utilizing variable modes decomposition (VMD). Feature extraction (FE) is performed by computing 7 features. The dimensionality of this signal is then lessened using Modified-Principal Components Analysis (M-PCA). Finally, classification is conducted utilizing the artificial neural networks (ANN) that is optimized using the hybrid dragonfly algorithm (HDA). Disparate performance metrics such as sensitivity, accuracy, and false discovery rates (FDR) are ascertained and as well weighted against with the existent works.
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References
I.M. Najm, Mapping brain networks in patients with focal epilepsy, Lancet Neurol. 17 (2018), 295–297
L.C. Lin, C.S.Ouyang, C.T. Chiang, R.C. Yang, R.C. Wu, H.C. Wu, Cumulative effect of transcranial direct current stimulation in patients with partial refractory epilepsy and its association with phase lag index-A preliminary study, Epilepsy Behav. 84 (2018), 142–147
A. Sen, V. Capelli, M. Husain, Cognition and dementia in older patients with epilepsy, Brain. 141 (2018), 1592–1608
C.S. Jacobs, I.P. Aisiku, Seizure activity, in: L. Ganti, J.N. Goldstein (Eds.), Neurologic Emergencies, Springer, Cham, 2018, pp. 59–84.
M. Bates, Controlling seizures with technology: researchers are working to predict and prevent epileptic seizures before they happen, IEEE Pulse. 9 (2018), 25–28
D. Wang, D. Ren, K. Li, Y. Feng, D. Ma, X. Yan, G. Wang, Epileptic seizure detection in long-term EEG recordings by using wavelet-based directed transfer function, IEEE Trans. Biomed. Eng. 65 (2018), 2591–2599
A. Witkowska-Wrobel, K. Aristovich, M. Faulkner, J. Avery, D. Holder, Feasibility of imaging epileptic seizure onset with EIT and depth electrodes, Neuroimage. 173 (2018), 311–321
S. Muppidi, B. Razavi, M.G. Miglis, S. Jaradeh, The clinical utility of qualitative electroencephalography during tilt table testing–a retrospective study, Clin. Neurophysiol. 129 (2018), 783–786
G.M. Benedetti, F.S. Silverstein, S.M. Rau, S.G. Lester, M.H. Benedetti, R.A. Shellhaas, Sedation and analgesia influence EEG monitoring in pediatric neurocritical care, Pediatr. Neurol. 87 (2018), 57–64
J. Teo, C.L. Hou, J. Mountstephens, Preference Classification Using Electroencephalography (EEG) and Deep learning, J. Telecommun. Electron. Comput. Eng. 10 (2018), 87–91
S.M. Usman, A. Hassan, Efficient prediction and classification of epileptic seizures using EEG data based on univariate linear features, J. Comput. 13 (2018), 616–621
A. Sanz-García, L. Vega-Zelaya, J. Pastor, R.G. Sola, G.J. Ortega, Towards operational definition of postictal stage: spectral entropy as a marker of seizure ending, Entropy. 19 (2017), 81.
S. Kumar, C.P. Nagesh, B. Thomas, A. Radhakrishnan, R.N. Menon, C. Kesavadas, Arterial spin labeling hyperperfusion in Rasmussen’s encephalitis: is it due to focal brain inflammation or a postictal phenomenon?, J. Neuroradiol. 45 (2018), 6–14
S. Wu, N.P. Issa, S.L. Rose, A. Ali, J.X. Tao, Impact of periictal nurse interventions on postictal generalized EEG suppression in generalized convulsive seizures, Epilepsy Behav. 58 (2016), 22–25
L.C. Lin, S.C.J. Chen, C.T. Chiang, H.C. Wu, R.C. Yang, C.S. Ouyang, Classification preictal and interictal stages via integrating interchannel and time-domain analysis of EEG features, Clin. EEG Neurosci. 48 (2017), 139–145
E. Alickovic, J. Kevric, A. Subasi, Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction, Biomed. Signal Process. Control. 39 (2018), 94–102
M. Fan, C.A. Chou, Detecting abnormal pattern of epileptic seizures via temporal synchronization of EEG signals, IEEE Trans. Biomed. Eng. 66 (2019), 601–608
A. Mert, A. Akan, Emotion recognition from EEG signals by using multivariate empirical mode decomposition, Pattern Anal. Appl. 21 (2018), 81–89
A. Gupta, P. Singh, M. Karlekar, A novel signal modeling approach for classification of seizure and seizure-free EEG signals, IEEE Trans. Neural Syst. Rehabil. Eng. 26 (2018), 925–935
A.K. Tiwari, R.B. Pachori, V. Kanhangad, B.K. Panigrahi, Automated diagnosis of epilepsy using key-point based local binary pattern of EEG signals, IEEE J. Biomed. Health Informat. 21 (2017), 888–896
S. Patidar, T. Panigrahi, Detection of epileptic seizure using Kraskov entropy applied on tunable-Q wavelet transform of EEG signals, Biomed. Signal Process. Control. 34 (2017), 74–80
T. Zhang, W. Chen, LMD based features for the automatic seizure detection of EEG signals using SVM, IEEE Trans. Neural Syst. Rehabil. Eng. 25 (2017), 1100–1108
L.S. Vidyaratne, K.M. Iftekharuddin, Real-time epileptic seizure detection using EEG, IEEE Trans. Neural Syst. Rehabil. Eng. 25 (2017), 2146–2156
M.Z. Parvez, M. Paul, Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation, IEEE Trans. Neural Syst. Rehabil. Eng. 24 (2018), 158–168
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Parthiban, K.G., Vijayachitra, S. & Dhanapal, R. Hybrid Dragonfly Optimization-Based Artificial Neural Network for the Recognition of Epilepsy. Int J Comput Intell Syst 12, 1261–1269 (2019). https://doi.org/10.2991/ijcis.d.191022.001
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DOI: https://doi.org/10.2991/ijcis.d.191022.001