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Yıl 2023, Cilt: 8 Sayı: 1 - JCS_Vol_8_No_1_2023, 16 - 27, 01.07.2023

Ceren Canyurt Reyhan Zengin

https://doi.org/10.52876/jcs.1226579

Öz

Kaynakça

  • [1] E. Foundation, What is epilepsy?, 2022. URL: https://www.epilepsy.com/ what-is-epilepsy.
  • [2] WHO, Atlas Epilepsy Care in The World, 2005.
  • [3] F. A. Gibbs, H. Davis, W. B. Lennox, The electroencephalogram in epilepsy and in conditions of impaired consciousness, American Journal of EEG Technology 8 (2015) 59-73.
  • [4] R. S. Fisher, H. E. Scharfman, M. deCurtis, How can we identify ictal and interictal abnormal activity?, Adv Exp Med Biol 813 (2014) 3- 23.
  • [5] J. Pillai, M. R. Sperling, interictal EEG and the diagnosis of epilepsy, Epilepsia 41 (2006) 14-22.
  • [6] D. Schmidt, S. C. Schachter, Drug treatment of epilepsy in adults, Phys. Rev E. (2014) 254.
  • [7] C. Anyanwu, G. K. Motamedi, Diagnosis and surgical treatment of drug-resistant epilepsy, Brain Sciences 8 (2018) 49.
  • [8] J. Yang, J. H. Phi, The present and future of vagus nerve stimulation, J. Korean Neurosurg Soc 62 (2019) 344-352.
  • [9] A. Yarsavvsky, I. Mareels, M. Cook, Epileptic Seizures and the EEG Measurement, Models, Detection and Prediction, 20 I I.
  • [10] V. Srinivasan, C. Eswaran, N. Sriraam, Artificial neural network based epileptic detection using time-domain and frequency-domain features, J Med Syst 29 (2005) 647-660.
  • [11] H. Ocak, Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm, Signal Processing 88 (2008) 1858-1867.
  • [12] S. Altunay, Z. Telatar, 0. Erogul, Epileptic eeg detection using the linear prediction error energy, Expert Systems with Applications 37 (2010) 5661-5665.
  • [13] S. K0<;er, M. R. Canal, Classifying epilepsy diseases using artificial neural networks and genetic algorithm, J Med Syst 35 (2011) 489- 498.
  • [14] Y. Kaya, R. Tekin, Epileptik nobetlerin tespiti i9in a m Ogrenme makinesi tabanl1 uzman bir sistem, Bili im Teknolojileri Dergisi 5 (2012) 33-40.
  • [15] V. P. Nigam, D. Graupe, A neural-network-based detection of epilepsy, A Journal of Progress in Neurosurgery, Neurology and Neurosciences 26 (2013) 55-60.
  • [16] M. Z. Parvez, M. Paul, Eeg signal classification using frequency band analysis towards epileptic seizure prediction, 16th lnt'l Conf. Computer and Information Technology (2014) 126-130.
  • [17] P. Singh, S. D. Joshi, R. K. Patney, K. Saha, Fourier-based feature extraction for classification of eeg signals using eeg rhythms, Circuits Syst Signal Process 35(2016) 3700-3715.
  • [18] S. Raghu, N. Sriraam, A. S. Hegde, Features ranking for the classification of epileptic seizure from temporal eeg, 2016 International Conference on Circuits. Controls, Communications and Computing (I4C) (2016) 1-4.
  • [19] A. G. Mahapatra, K. Horio, Classification of ictal and interictal eeg using rms frequency, dominant frequency, root mean instantaneous frequency square and their parameters ratio, Biomedical Signal Processing and Control. 44 (2018) 168-180.
  • [20] 0. K. Fasil, R. Rajesh, Time-domain exponential energy for epileptic eeg signal classification., Neuroscience Letters 694 (2019) 1-8.
  • [21] P. Boonyakitanont, A. Lek-uthai, K. Chomtho, J. Songsiri, A review of feature extraction and performance evaluation in epileptic seizure detection using eeg, Biomedical Signal Processing and Control. 57 (2020).
  • [22] V. Harpale, V. Bairagi, An adaptive method for feature selection and extraction for classification of epileptic eeg signal in significant states, Journal of King Saud University - Computer and Information Sciences. 33 (2021) 668-676.
  • [23] C. Ficici, 0. Eroglu, Z. Telatar, Epileptic activity detection in eeg signals using linear and non-linear feature extraction methods, 2019 11th International Conference on Electrical and Electronics Engineering (ELECO). 392 (2019) 449-455.
  • [24] T. V. Yakovleva, I.E. Kutepov, A. Yu Karas, N. M. Yakovlev, V. V. Dobriyan, I. V. Papkova, Z. M. V., 0. A. Saltykova, A. V. Krysko, T. Yu Yaroshenko, N. P. Erofeev, K. V. A., Eeg analysis in structural focal epilepsy using the methods of nonlinear dynamics (lyapunov exponents, lempel-ziv complexity, and multiscale entropy), The Scientific World Journal. (2020).
  • [25] N. Kannathal, M. Lim Choo, U. R. Acharya, P. Sadasivan, Entropies for detection of epilepsy in eeg, Computer Methods and Programs in Biomedicine. 80 (2005) 187-194.
  • [26] Y. Song, J. Crowcroft, J. Zhang, Eepileptic eeg signal analysis and identification based on nonlinear features, 2012 IEEE International Conference on Bioinformatics and Biomedicine. (2012) 1-6.
  • [27] H. Ocak, Automatic detection of epileptic seizures in eeg using discrete wavelet transform and approximate entropy, Expert Systems with Applications. 36 (2009) 2027-2036.
  • [28] A. Mirzaei, A. Ayatollahi, P. Gifani, L. Salehi, Eeg analysis based on wavelet-spectral entropy for epileptic seizures detection, 2010 3rd International Conference on Biomedical Engineering and Informatics, 878-882 (2010) 878-882.
  • [29] S. P. Kumar, N. Sriraam, P. Benakop, B. Jinaga, Entropies based detection of epileptic seizures with artificial neural network classifiers, Expert Systems with Applications. 37 (20 I 0) 3284-3291.
  • [30] Y. Song, J. Crowcroft, J. Zhang, Automatic epileptic seizure detection in eegs based on optimized sample entropy and extreme learning machine, Journal of Neuroscience Methods. 210 (2012) I 32- 146.
  • [31] N. Nicolaou, J. Georgiou, Detection of epileptic electroencephalogram based on pennutation entropy and support vector machines, Expert Systems with Applications. 39 (2012) 202-209.
  • [32] J. Xiang, C. Li, H. Li, R. Cao, B. Wang, X. Han, J. Chen, The detection of epileptic seizure signals based on fuzzy entropy, Journal of Neuro­ science Methods. 243 (2015) 18-25.
  • [33] P. Li, C. Yan, C. Kannakar, C. Liu, Distribution entropy analysis of epileptic eeg signals, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). (2015) 4170-4173.
  • [34] J. A. Urigiien, B. Garcia-Zapirain, J. Artieda, J. lriarte, M. Valencia, Comparison of background eeg activity of different groups of patients with idiopathic epilepsy using Shannon spectral entropy and cluster­ based permutation statistical testing, PLOS ONE. (2017).
  • [35] S. Raghu, N. Sriraam, Y. Temel, S. V. Rao, A. S. Hegde, P. L. Kubben,Performance evaluation of dwt-based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using svm classifier, Computers in Biology and Medicine. 110 (2019) 127- 143.
  • [36] S. T. Aung, Y. Wongsawat, Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy, PeerJ Computer Science. (2021 ).
  • [37] Q. Zhang, J. Ding, W. Kong, Y. Liu, Q. Wang, T. Jiang, Epilepsy prediction through optimized multidimensional sample entropy and bilstm, Biomedical Signal Processing and Control. 64 (2021) 102293.
  • [38] X. Li, G. Ouyang, D. A. Richards, Predictability analysis of absence seizures with permutation entropy, Epilepsy Research. 77 (2007) 70-74.
  • [39] A. A. Bruzzo, S. M. Gesierich, B. and, C. A. Tassinari, N. Birbaumer, G. Rubboli, Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients. a preliminary study, Neurological Sciences. 29 (2008) 3-9.
  • [40] C. C. Jouny, G. K. Bergey, Characterization of early partial seizure onset: frequency, complexity and entropy, Clinical Neurophysiology. 123 (2012) 658-669.
  • [41] N. Marrunone, D. Labate, A. L. Ekuakille, L. C. Morabito, Analysis of absence seizure generation using eeg spatial-temporal regularity measures, International Journal of Neural System. 22 (2012) 1250024.
  • [42] G. Ouyang, J. Li, X. Liu, X. Li, Dynamic characteristics of absence eeg recordings with multiscale permutation entropy analysis, Epilepsy Research. 104 (2013) 246-252.
  • [43] N. Bhanot, N. Mariyappa, H. Anitha, G. K. Bhargava, J. Velmurugan, S. Sinha, Seizure detection and epileptogenic zone localisation on heavily skewed meg data using rusboost machine learning technique, International Journal of Neuroscience. 132 (2022).
  • [44] G. Peng, M. Nourani, J. Harvey, H. Dave, Feature selection using f­ statistic values for eeg signal analysis, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society. (2020) 5963-5966.
  • [45] J. S. Ra, T. Li, Y. Li, A novel permutation entropy-based eeg channel selection for improving epileptic seizure prediction, Sensors. (2021) 7972.
  • [46] F. Kahn, A. T. <;:., D. Turkpence, S. Seker, U. Korkmaz, Detection of epileptic seizure using stti and statistical analysis, Advances in Neural Signal Processing. (2020).
  • [47] J. S. Richman, J. R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy., Am. J. Physiol. - Heart Circ. Physiol (2000) 2039-2049.
  • [48] sC. Bandt, B. Pompe, Permutation entropy: A natuml complexity measure for time series, Physical Review Letters. (2002).
  • [49] A. H. Shoeb, Application of machine learning to epileptic seizure onset detection and treatment., Massachusetts institute of Technology. Cambridge. MA. USA. (2009).
  • [SO] MathWorks, Matlab [online], https://www.mathworks.com/products/matlab.html, 2021.

Epileptic Activity Detection using Mean Value, RMS, Sample Entropy, and Permutation Entropy Methods

Yıl 2023, Cilt: 8 Sayı: 1 - JCS_Vol_8_No_1_2023, 16 - 27, 01.07.2023

Ceren Canyurt Reyhan Zengin

https://doi.org/10.52876/jcs.1226579

Öz

In this study, linear and non-linear signal analysis methods are implemented for epilepsy seizure detection using CHB-MIT EEG data taken from Boston children's hospital. In linear signal analysis, EEG signals are considered linear, although they are not linear. In linear signal analysis methods, root mean square (RMS) and mean of the EEG signals are analyzed. It is detected that the RMS value increased and the mean value moved away from zero in the positive and negative directions during the seizure period. Seizure periods in EEG signals are determined with RMS and mean values with 75 % and 58.4 % accuracy, respectively. Since EEG signals are not linear, the linear analysis is assumed insufficient and so entropy is preferred to linear signal analysis methods. Sample entropy (SmpE) and permutation entropy (PE) are preferred among entropy types. While an increase is observed in the sample entropy values at the beginning of the seizure, a decrease is observed in the permutation entropy values at the same time. When the entropy methods are examined separately, the onset of a seizure is determined with an accuracy of 66.6 % for both methods. However, when the entropy methods are examined together with the increase in the sample entropy value or the decrease in the permutation entropy, the accuracy rate increases to 79.2 % The resultant accuracy rates show that when one entropy method fails to catch the onset of a seizure the other can.

Anahtar Kelimeler

Epilepsy Mean of Signal RMS Sample Entropy Permutation Entropy

Kaynakça

  • [1] E. Foundation, What is epilepsy?, 2022. URL: https://www.epilepsy.com/ what-is-epilepsy.
  • [2] WHO, Atlas Epilepsy Care in The World, 2005.
  • [3] F. A. Gibbs, H. Davis, W. B. Lennox, The electroencephalogram in epilepsy and in conditions of impaired consciousness, American Journal of EEG Technology 8 (2015) 59-73.
  • [4] R. S. Fisher, H. E. Scharfman, M. deCurtis, How can we identify ictal and interictal abnormal activity?, Adv Exp Med Biol 813 (2014) 3- 23.
  • [5] J. Pillai, M. R. Sperling, interictal EEG and the diagnosis of epilepsy, Epilepsia 41 (2006) 14-22.
  • [6] D. Schmidt, S. C. Schachter, Drug treatment of epilepsy in adults, Phys. Rev E. (2014) 254.
  • [7] C. Anyanwu, G. K. Motamedi, Diagnosis and surgical treatment of drug-resistant epilepsy, Brain Sciences 8 (2018) 49.
  • [8] J. Yang, J. H. Phi, The present and future of vagus nerve stimulation, J. Korean Neurosurg Soc 62 (2019) 344-352.
  • [9] A. Yarsavvsky, I. Mareels, M. Cook, Epileptic Seizures and the EEG Measurement, Models, Detection and Prediction, 20 I I.
  • [10] V. Srinivasan, C. Eswaran, N. Sriraam, Artificial neural network based epileptic detection using time-domain and frequency-domain features, J Med Syst 29 (2005) 647-660.
  • [11] H. Ocak, Optimal classification of epileptic seizures in EEG using wavelet analysis and genetic algorithm, Signal Processing 88 (2008) 1858-1867.
  • [12] S. Altunay, Z. Telatar, 0. Erogul, Epileptic eeg detection using the linear prediction error energy, Expert Systems with Applications 37 (2010) 5661-5665.
  • [13] S. K0<;er, M. R. Canal, Classifying epilepsy diseases using artificial neural networks and genetic algorithm, J Med Syst 35 (2011) 489- 498.
  • [14] Y. Kaya, R. Tekin, Epileptik nobetlerin tespiti i9in a m Ogrenme makinesi tabanl1 uzman bir sistem, Bili im Teknolojileri Dergisi 5 (2012) 33-40.
  • [15] V. P. Nigam, D. Graupe, A neural-network-based detection of epilepsy, A Journal of Progress in Neurosurgery, Neurology and Neurosciences 26 (2013) 55-60.
  • [16] M. Z. Parvez, M. Paul, Eeg signal classification using frequency band analysis towards epileptic seizure prediction, 16th lnt'l Conf. Computer and Information Technology (2014) 126-130.
  • [17] P. Singh, S. D. Joshi, R. K. Patney, K. Saha, Fourier-based feature extraction for classification of eeg signals using eeg rhythms, Circuits Syst Signal Process 35(2016) 3700-3715.
  • [18] S. Raghu, N. Sriraam, A. S. Hegde, Features ranking for the classification of epileptic seizure from temporal eeg, 2016 International Conference on Circuits. Controls, Communications and Computing (I4C) (2016) 1-4.
  • [19] A. G. Mahapatra, K. Horio, Classification of ictal and interictal eeg using rms frequency, dominant frequency, root mean instantaneous frequency square and their parameters ratio, Biomedical Signal Processing and Control. 44 (2018) 168-180.
  • [20] 0. K. Fasil, R. Rajesh, Time-domain exponential energy for epileptic eeg signal classification., Neuroscience Letters 694 (2019) 1-8.
  • [21] P. Boonyakitanont, A. Lek-uthai, K. Chomtho, J. Songsiri, A review of feature extraction and performance evaluation in epileptic seizure detection using eeg, Biomedical Signal Processing and Control. 57 (2020).
  • [22] V. Harpale, V. Bairagi, An adaptive method for feature selection and extraction for classification of epileptic eeg signal in significant states, Journal of King Saud University - Computer and Information Sciences. 33 (2021) 668-676.
  • [23] C. Ficici, 0. Eroglu, Z. Telatar, Epileptic activity detection in eeg signals using linear and non-linear feature extraction methods, 2019 11th International Conference on Electrical and Electronics Engineering (ELECO). 392 (2019) 449-455.
  • [24] T. V. Yakovleva, I.E. Kutepov, A. Yu Karas, N. M. Yakovlev, V. V. Dobriyan, I. V. Papkova, Z. M. V., 0. A. Saltykova, A. V. Krysko, T. Yu Yaroshenko, N. P. Erofeev, K. V. A., Eeg analysis in structural focal epilepsy using the methods of nonlinear dynamics (lyapunov exponents, lempel-ziv complexity, and multiscale entropy), The Scientific World Journal. (2020).
  • [25] N. Kannathal, M. Lim Choo, U. R. Acharya, P. Sadasivan, Entropies for detection of epilepsy in eeg, Computer Methods and Programs in Biomedicine. 80 (2005) 187-194.
  • [26] Y. Song, J. Crowcroft, J. Zhang, Eepileptic eeg signal analysis and identification based on nonlinear features, 2012 IEEE International Conference on Bioinformatics and Biomedicine. (2012) 1-6.
  • [27] H. Ocak, Automatic detection of epileptic seizures in eeg using discrete wavelet transform and approximate entropy, Expert Systems with Applications. 36 (2009) 2027-2036.
  • [28] A. Mirzaei, A. Ayatollahi, P. Gifani, L. Salehi, Eeg analysis based on wavelet-spectral entropy for epileptic seizures detection, 2010 3rd International Conference on Biomedical Engineering and Informatics, 878-882 (2010) 878-882.
  • [29] S. P. Kumar, N. Sriraam, P. Benakop, B. Jinaga, Entropies based detection of epileptic seizures with artificial neural network classifiers, Expert Systems with Applications. 37 (20 I 0) 3284-3291.
  • [30] Y. Song, J. Crowcroft, J. Zhang, Automatic epileptic seizure detection in eegs based on optimized sample entropy and extreme learning machine, Journal of Neuroscience Methods. 210 (2012) I 32- 146.
  • [31] N. Nicolaou, J. Georgiou, Detection of epileptic electroencephalogram based on pennutation entropy and support vector machines, Expert Systems with Applications. 39 (2012) 202-209.
  • [32] J. Xiang, C. Li, H. Li, R. Cao, B. Wang, X. Han, J. Chen, The detection of epileptic seizure signals based on fuzzy entropy, Journal of Neuro­ science Methods. 243 (2015) 18-25.
  • [33] P. Li, C. Yan, C. Kannakar, C. Liu, Distribution entropy analysis of epileptic eeg signals, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). (2015) 4170-4173.
  • [34] J. A. Urigiien, B. Garcia-Zapirain, J. Artieda, J. lriarte, M. Valencia, Comparison of background eeg activity of different groups of patients with idiopathic epilepsy using Shannon spectral entropy and cluster­ based permutation statistical testing, PLOS ONE. (2017).
  • [35] S. Raghu, N. Sriraam, Y. Temel, S. V. Rao, A. S. Hegde, P. L. Kubben,Performance evaluation of dwt-based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using svm classifier, Computers in Biology and Medicine. 110 (2019) 127- 143.
  • [36] S. T. Aung, Y. Wongsawat, Prediction of epileptic seizures based on multivariate multiscale modified-distribution entropy, PeerJ Computer Science. (2021 ).
  • [37] Q. Zhang, J. Ding, W. Kong, Y. Liu, Q. Wang, T. Jiang, Epilepsy prediction through optimized multidimensional sample entropy and bilstm, Biomedical Signal Processing and Control. 64 (2021) 102293.
  • [38] X. Li, G. Ouyang, D. A. Richards, Predictability analysis of absence seizures with permutation entropy, Epilepsy Research. 77 (2007) 70-74.
  • [39] A. A. Bruzzo, S. M. Gesierich, B. and, C. A. Tassinari, N. Birbaumer, G. Rubboli, Permutation entropy to detect vigilance changes and preictal states from scalp EEG in epileptic patients. a preliminary study, Neurological Sciences. 29 (2008) 3-9.
  • [40] C. C. Jouny, G. K. Bergey, Characterization of early partial seizure onset: frequency, complexity and entropy, Clinical Neurophysiology. 123 (2012) 658-669.
  • [41] N. Marrunone, D. Labate, A. L. Ekuakille, L. C. Morabito, Analysis of absence seizure generation using eeg spatial-temporal regularity measures, International Journal of Neural System. 22 (2012) 1250024.
  • [42] G. Ouyang, J. Li, X. Liu, X. Li, Dynamic characteristics of absence eeg recordings with multiscale permutation entropy analysis, Epilepsy Research. 104 (2013) 246-252.
  • [43] N. Bhanot, N. Mariyappa, H. Anitha, G. K. Bhargava, J. Velmurugan, S. Sinha, Seizure detection and epileptogenic zone localisation on heavily skewed meg data using rusboost machine learning technique, International Journal of Neuroscience. 132 (2022).
  • [44] G. Peng, M. Nourani, J. Harvey, H. Dave, Feature selection using f­ statistic values for eeg signal analysis, 2020 42nd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society. (2020) 5963-5966.
  • [45] J. S. Ra, T. Li, Y. Li, A novel permutation entropy-based eeg channel selection for improving epileptic seizure prediction, Sensors. (2021) 7972.
  • [46] F. Kahn, A. T. <;:., D. Turkpence, S. Seker, U. Korkmaz, Detection of epileptic seizure using stti and statistical analysis, Advances in Neural Signal Processing. (2020).
  • [47] J. S. Richman, J. R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy., Am. J. Physiol. - Heart Circ. Physiol (2000) 2039-2049.
  • [48] sC. Bandt, B. Pompe, Permutation entropy: A natuml complexity measure for time series, Physical Review Letters. (2002).
  • [49] A. H. Shoeb, Application of machine learning to epileptic seizure onset detection and treatment., Massachusetts institute of Technology. Cambridge. MA. USA. (2009).
  • [SO] MathWorks, Matlab [online], https://www.mathworks.com/products/matlab.html, 2021.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektrik Mühendisliği
Bölüm Articles
Yazarlar

Ceren Canyurt 0000-0003-2744-3752

Reyhan Zengin 0000-0001-8631-3339

Erken Görünüm Tarihi 2 Temmuz 2023
Yayımlanma Tarihi 1 Temmuz 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 8 Sayı: 1 - JCS_Vol_8_No_1_2023

Kaynak Göster

APA Canyurt, C., & Zengin, R. (2023). Epileptic Activity Detection using Mean Value, RMS, Sample Entropy, and Permutation Entropy Methods. The Journal of Cognitive Systems, 8(1), 16-27. https://doi.org/10.52876/jcs.1226579
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