EEG SİNYALLERİ İLE EPİLEPSİ KRİZİNİN TAHMİNLENMESİNDE RASSAL ORMAN ALGORİTMASI İLE HİPER PARAMETRE OPTİMİZASYONUN UYGULANMASI

Abstract

Dünyadaki 50 milyondan fazla kişiden oluşan tüm nüfusun yaklaşık % 1'i epilepsi ve epileptik nöbetlerden etkilenmektedir (Litt, Echauz 2002) (Kandel ve ark., 2000). Epileptik nöbetler, beynin elektriksel aktivitesindeki bir rahatsızlıktan kaynaklanır. Epilepsi nöbetinin saptanması genellikle elektroensefalografik (EEG) sinyal incelendikten sonra uzman görüşü tarafından gerçekleştirilir. Bu manuel bir süreçtir ve büyük ölçüde doktorun uzmanlığına dayanır. Bu nedenle, doktorların daha az hatayla teşhis koymasına yardımcı olmak için otomatik tanı veya yardım sistemleri gereklidir. Bu çalışmada, epileptik nöbetlerin varlığını sınıflandırmak için iyi bilinen (Andrzejak ve ark. 2001) bir veri kümesi kullanılmıştır. Veri setinin farklı konfigürasyonları literatürde bir kısmı Lojistik Regresyon, Dalgacık yöntemi, Karar Ağacı, Destek Vektör Makinesi, Yoğun Sinir Ağları, vb. birçok veri madenciliği ve makine öğrenme algoritması ile incelenmiştir. İyi tanı beklentisini karşılamak için Rassal Orman kullanılarak sınıflandırma modeli geliştirilmiştir ve sonuçlar aynı veri seti üzerinde incelenen farklı yöntemlerle karşılaştırılmıştır. Çalışılan deneylerin bazı vakalarında %99,78 oranında doğruluk, %99,95 özgüllük ve %99,61 hassasiyet elde edilmiştir ve sonuçlar modelinin başarılı şekilde sınıflandırdığını göstermektedir.

Keywords

• EEG
• Hiper Parametre Optimizasyonu
• Makine öğrenmesi

IMPLEMENTATION OF HYPER PARAMETER OPTIMIZATION WITH RANDOM FOREST ALGORITHM FOR THE ESTIMATION OF THE EPILEPTIC SEIZURES WITH EEG SIGNALS

Abstract

About %1 of the whole population of the world which constitutes more than 50 million people are affected by epilepsy and epileptic seizures (Litt, Echauz 2002) (Kandel ve ark., 2000). Epileptic seizures are caused by a disturbance in the electrical activity of the brain. Detecting epileptic seizure is generally carried out by the expert opinion after examining the electroencephalographic (EEG) signal. This is a manual process and heavily relies on the expertise of the physician. Therefore automated diagnosis or aiding systems are required to assist physicians to diagnose with fewer errors. In this study, a well known (Andrzejak et al. 2001) dataset is used for classifying the existence of epileptic seizures. Different configurations of the data set have been studied with many data mining and machine learning algorithms in the literatüre, some of which are Logistic Regression, Wavelet Method, Decision Tree, Support Vector Machine, Dense Neural networks, etc.. In this study, a classification model was developed by using Random Forest to meet the good diagnosis expectation, and results were compared with different methods studied on the same data set. In some cases of the studied experiments above 99,78 percent of accuracy, 99,95% specificity, and 99,61% sensitivity are obtained, indicating a good sign of classification model.

Keywords

• EEG
• Hyper Parameter Optimization,
• Machine Learning

References

1. Acharya U.R., Oh S.L., Hagiwara Y., Tan J.H., Adeli H. (2018). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Computers in Biology and Medicine, 100 270278
2. Amin H.U, Yusoff M.Z, Ahmad R.F , A novel approach based on wavelet analysis and arithmetic coding for automated detection and diagnosis of epileptic seizure in EEG signals using machine learning techniques. Biomedical Signal Processing and Control 56 (2020) 101707
3. Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE (2001). Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Phys. Rev. E, 64, 061907
4. Bergstra J., Bardenet R., Bengio Y., Kegl B. (2011) Algorithms for hyper-parameter optimization. In NIPS
5. Bergstra J. ve Bengio Y. (2012) Random search for hyper-parameter optimization.
6. Bhardwaj A., Tiwari A., Krishna R., Varma V., A novel genetic programming approach for epileptic seizure detection, Comput. Methods Programs Biomed.124 (2016) 2–18.
7. Bhattacharyya A., Pachori R.B., Upadhyay A., Acharya U.R., Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals, Appl. Sci. 7 (4) (2017) 385.
8. Breiman L., Friedman J. H., Olshen R. A., Stone C. J. (1984), Classification and Regression Trees. Belmont, CA: Wadsworth International.

9. Breiman, L. (1996). Bagging predictors. Machine Learning 26(2), 123–140.
10. Breiman, L. (2001). Random Forests. Machine Learning 45 (1): 5–32.Doi: 10.1023/A: 1010933404324.
11. Chen G., Xie W., Bui T.D., Krzyzak A., Automatic epileptic seizure detection in EEG using non subsampled wavelet-Fourier features, J. Med. Biol. Eng. 37 (1)(2017) 123–131.
12. Chen G., Automatic EEG seizure detection using dual-tree complex wavelet-Fourier features, Expert Syst. Appl. 41 (2014) 2391–2394.
13. Dehuri S., Jagadev A.K., Cho S.-B., Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble, Procedia Comput.Sci. 23 (2013) 84–95.
14. Dekking, Michel (2005). A Modern Introduction to Probability and Statistics. Springer. pp. 181–190.
15. Dhiman R., Saini J.S., Priyanka, Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures, Appl. Soft Comput. 19(2014) 8–17.
16. Fernandez-Blanco E., Rivero D., Rabu˜nal J., Dorado J., Pazos A., Munteanu C.R.,Automatic seizure detection based on star graph topological indices, J.Neurosci. Methods 209 (2012) 410–419.
17. Fu K., Qu J., Chai Y., Dong Y., Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM, Biomed. SignalProcess. Control 13 (2014) 15–22.
18. Guo L, Rivero D, Seoane J, Pazos A. Classification of EEG signals using relative wavelet energy and artificial neural networks. In: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation; 2009. p. 177–84.
19. Guo L, Rivero D, Pazos A. Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. Journal of Neuroscience Methods 193 (2010) 156–163
20. Hassan A.R., Siuly S., Zhang Y., Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating, Comput.Methods Programs Biomed. 137 (2016) 247–259.
21. Ho T.K. (1998). The random subspace method for constructing decision forests. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(8), 832–844.
22. Hussein R., Palangi H., Ward R., Wang Z.J., Epileptic Seizure Detection: A Deep Learning Approach, 2018, arXiv preprint arXiv:1803.09848.
23. Jahankhani P, Kodogiannis V, Revett K. (2006). EEGsignal classification using wavelet feature extraction and neural networks. In: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA’06);. p. 52–7.
24. Jiang Y., Cukic B., Menzies T., Can Data Transformation Help in the Detection of Fault-prone Modules? In Proceedings of the workshop on Defects in Large Software Systems, pages 16–20, 2008.
25. Kalayci T, Ozdamar O. (1995). Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Engineering in Medicine and Biology Magazine;14(2):160–6.
26. Kandel E, Schwartz J, Jessell T. (2000). Principles of Neural Science. New York: McGraw-Hill,Health Professions Division
27. Kang J.-H., Chung Y.G., Kim S.-P., An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms, Comput. Biol.Med. 66 (2015) 352–356.
28. Kannathal N., Choo M.L Acharya U.R., Sadasivana P.K. (2005). Entropies for detection of epilepsy in EEG, Comput. Methods Programs Biomed. 80 187–194
29. Kannathal N, Choo M, Acharya U, Sadasivan P. Entropies for detection of epilepsy in EEG. Computer Methods and Programs in Biomedicine 2005;80(3):187–94.
30. Kaya Y., Uyar M., Tekin R., Yıldırım S., 1D-local binary pattern based feature extraction for classification of epileptic EEG signals, Appl. Math. Comput. 243(2014) 209–219.
31. Kumar Y., Dewal M., Anand R., Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network, Signal Image Video Process.(2012) 1–12.
32. Kumar Y., Dewal M., Anand R., Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine, Neurocomputing133 (2014) 271–279.
33. Lee S.-H., Lim J.S., Kim J.-K., Yang J., Lee Y., Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance, Comput. Methods Programs Biomed.116 (2014) 10–25.
34. Litt B, Echauz J (2002). Prediction of epileptic seizures, The Lancet Neurology, V1 (1) , pp 22-30,
35. Martinez-del-Rincon J., Santofimia M.J., del Toro X., Barba J., Romero F., Navas P., Lopez J.C., Non-linear classifiers applied to EEG analysis for epilepsyseizure detection, Expert Syst. Appl. 86 (2017) 99–112, 2017/11/15.
36. Morgan J. N. ve Sonquist J. A. (1963), Problems in the analysis of survey data, and a proposal, J. Amer. Statist. Ass., vol. 58, pp. 415-434.
37. Nigam V, Graupe D (2004). A neural-network-based detection of epilepsy. Neurological Research;26(1):55–60.
38. Ocak H., Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy, Expert Systems with Applications 36 (2) (2009) 2027–2036.
39. Peker M., Sen B., Delen D., A novel method for automated diagnosis of epilepsy using complex-valued classifiers, IEEE J. Biomed. Health Inform. 20(1) (2016) 108–118.
40. Polat K, Günes¸ S. Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Applied Mathematics and Computation 2007;187(2):1017–26.
41. Sadati N, Mohseni H.R., Magshoudi A., Epileptic seizure detection using neural fuzzy networks, in: Proceedings of the IEEE International Conference on Fuzzy Systems, Canada, 2006, pp. 596–600.
42. Sharma M., Pachori R.B., Acharya U.R., A new approach to characterize epileptic seizures using analytic timefrequency flexible wavelet transformand fractal dimension, Pattern Recogn. Lett. 94 (2017) 172–179.
43. Sharma M., Pachori R.B., Sircar P., Seizures classification based on higher order statistics and deepneural network. Biomedical Signal Processing and Control 59 (2020) 101921 Song Y., Zhang J., Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction, Expert Syst. Appl.40 (2013) 5477–5489.
44. Srinivasan V, Eswaran C, Sriraam N. Artificial neural network based epileptic detection using time-domain and frequency-domain features. Journal of Medical Systems 2005;29(6):647–60.
45. Srinivasan V., Eswaran C., Sriraam N., Approximate entropy-based epileptic EEG detection using artificial neural networks, IEEE Transaction on Information Technology in Biomedicine 11 (3) (2007) 288–295.
46. Subasi A. (2005). Epileptic seizure detection using dynamic wavelet network. Expert Systems with Applications;29(2):343–55.
47. Subasi A. (2006) Automatic detection of epileptic seizure using dynamic fuzzy neural networks. Expert Systems with Applications;31(2):320–8.
48. Subasi A. (2007) EEG signal classification using wavelet feature extraction and amixture of expert model. Expert Systems with Applications;32(4):1084–93.
49. Subasi A., Gursoy M.I., EEG Signal classification using PCA, ICA, LDA and support vector machine, Expert Systems with Applications 37 (2010) 8659–8666.
50. Syarif, I., Prugel-Bennett, A., Wills G., SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance. Telkomnika 2016, 14, 1502–1509.
51. Swami P., Gandhi T.K., Panigrahi B.K., Tripathi M., Anand S., A novel robust diagnostic model to detect seizures in electroencephalography, Expert Syst.Appl. 56 (2016) 116–130.
52. Tantithamthavorn C., McIntosh S., Hassan A. E., and Matsumoto K. (2016). Automated parameter optimization of classification techniques for defect prediction models. IEEE/ACM 38th IEEE International Conference on Software Engineering Tawfik N.S., Youssef S.M., Kholief M., A hybrid automated detection of epileptic seizures in EEG records, Comput. Electr. Eng. (2015).
53. Tosun A. ve Bener A., Reducing false alarms in software defect prediction by decision threshold optimization.In Proceedings of the International Symposium on Empirical Software Engineering and Measurement, pages 477480, 2009.
54. Tzallas A, Tsipouras M, Fotiadis D. Automatic seizure detection based on time–frequency analysis and artificial neural networks. Computational Intelligence and Neuroscience 2007;13, article ID 80510.
55. UCI Machine Learning Repository, 2020 https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition . son erişim tarihi 17 Mayıs 2020
56. Viglione SS, Walsh GO, (1975). Proceedings: Epileptic seizure prediction. Electroencephalography and clinical neurophysiology, 39 (4): 435–436.
57. Yavuz, E., Kasapbaşı, M. C., Eyüpoğlu, C., & Yazıcı, R. (2018). An epileptic seizure detection system based on cepstral analysis and generalized regression neural network. Biocybernetics and Biomedical Engineering, 38(2), 201-216.
58. Yuan Q., Zhou W., Yuan S., Li X., Wang J., Jia G., Epileptic EEG classification based on kernel sparse representation, Int. J. Neural Syst. 24 (2014) 1450015.
59. Xie S., Krishnan S., Wavelet-based sparse functional linear model with applications to EEGs seizure detection and epilepsy diagnosis, Med. Biol. Eng.Comput. 51 (2013) 49–60.
60. Zamir Z.R., Detection of epileptic seizure in EEG signals using linear least squares preprocessing, Comput. Methods Programs Biomed. 133 (2016)95–109.
61. Zhang T., Chen W., LMD based features for the automatic seizure detection of EEG signals using SVM, IEEE Trans. Neural Syst. Rehabil. Eng. 25 (8) (2016)1100–1108.
62. Zhou M., Tian C., Cao R., Wang B., Niu Y., Hu T., Guo H., Xiang J., Epileptic seizure detection based on EEG signals and CNN, Frontiers Neuroinform. 12(2018) 95.
63. Zhu G., Li Y., Wen P.P., Epileptic seizure detection in EEGs signals using a fastweighted horizontal visibility algorithm, Comput. Methods Programs Biomed.115 (2014) 64–75.