From Electroencephalogram to Epileptic Seizures Detection by Using Artificial Neural Networks
Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.
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 World Health Organization, 2018, http://www.who.int/news-room/fact-sheets/detail/epilepsy.
 C. E. Elger and C. Hoppe, “Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection,” Lancet Neurol, 2018 Mar, 17(3), pp. 279-288.
 American Clinical Neurophysiology Society. Guideline twelve: guidelines for long-term monitoring for epilepsy, Journal of Clinical Neurophysiology, Vol. 25, N. 3, June 2008.
 R. K. Maganti and P. Rutecki, “EEG and epilepsy monitoring,” Continuum (Minneap Minn), 2013 Jun, 19 (3 Epilepsy), pp. 598-622.
 Y. Li et al, “Epileptic Seizure Detection Based on Time-Frequency Images of EEG Signals Using Gaussian Mixture Model and Gray Level Co-Occurrence Matrix Features,” Int J Neural Syst., 2018, 28(7).
 A. Şengür, Y. Guo, and Y. Akbulut, “Time-frequency texture descriptors of EEG signals for efficient detection of epileptic seizure,” Brain Inform 3(2), 2016, pp. 101-108.
 Y. Li et al., “Epileptic Seizure Classification of EEGs Using Time-Frequency Analysis Based Multiscale Radial Basis Functions,” IEEE J Bio. Health Inf. 22(2), 2017, pp. 386-397.
 S. Raghu, N. Sriraam, G.P. Kumar, and A. S. Hegde, “A Novel Approach for Real-Time Recognition of Epileptic Seizures Using Minimum Variance Modified Fuzzy Entropy,” IEEE Trans Biomed Eng 65(11), 2018, pp. 2612-2621.
 S. Raghu S, N. Sriraam N, and G. P. Kumar, “Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier,” Cogn Neurodyn, 11(1), 2017, pp. 51-66.
 A. Sharmila, P. Mahalakshmi, “Wavelet-based feature extraction for classification of epileptic seizure EEG signal,” J Med Eng Technol. 41(8), 2017, pp. 670-680.
 Q. Yuan et al., “Epileptic seizure detection based on imbalanced classification and wavelet packet transform,” Seizure 50, 2017, pp. 99-108.
 N. Mahmoodian, A. Boese, M. Friebe, and J. Haddadnia, “Epileptic seizure detection using cross-bispectrum of electroencephalogram signal,” Seizure 66, 2019, pp. 4-11.
 G. Ouyang, X. Li, C. Dang, and D. A. Richards, “Using recurrence plot for determinism analysis of EEG recordings in genetic absence epilepsy rats,” Clin Neurophys. 119, 2008, pp. 1747-1755.
 A. Emami et al., “Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images,” Neuroimage Clin. 22, 101684, 2019.
 A. H. Ansari et al., “Neonatal Seizure Detection Using Deep Convolutional Neural Networks,” Int J Neural Syst., 1850011, 2018.
 S. Kusmakar et al., “Improved Detection and Classification of Convulsive Epileptic and Psychogenic Non-epileptic Seizures Using FLDA and Bayesian Inference,” Conf Proc IEEE Eng Med Biol Soc., 2018, pp. 3402-3405.
 R. Hussein et al., “Optimized deep neural network architecture for robust detection of epileptic seizures using EEG signals,” Clin Neurophysiol 130(1), 2019, pp. 25-37.
 A. Aarabi, R. Fazel-Rezai, and Y. Aghakhani, “A fuzzy rule-based system for epileptic seizure detection in intracranial EEG,” Clin Neurophysiol 2009, 120, pp. 1648-57.
 K. T. Tapani, S. Vanhatalo, and N. J. Stevenson, “Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection,” Int. J. Neural Syst., 2018 Jun 24:1850030.
 F. Manzouri et al., “A Comparison of Machine Learning Classifiers for Energy-Efficient Implementation of Seizure Detection,” Front Syst Neurosci, 2018 Sep 20; 12:43.
 Y. Kassahun et al., “Automatic classification of epilepsy types using ontology-based and genetics-based machine learning,” Artif Intell Med, 2014 Jun, 61(2), pp. 79-88.
 P. Tan, M. Steinbach, and V. Kumar, “Introduction to Data Mining”, Pearson Addison Wesley, 2005.
 M. Hall, et al., “The WEKA Data Mining Software: An Update”, SIGKDD Explorations, Volume 11, Issue 1, 2009.
 I. H. Witten and E. Frank, “Data Mining. Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, 2005.
 FSPEEG Website. Seizure prediction project Freiburg, University of Freiburg. http://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database/ (accessed 8 May 2019).
 Freiburger Zentrum fur Datenanalyse und Mollbildung: The Freiburg seizure prediction project. https://epilepsy.uni-freiburg.de/freiburg-seizure-prediction-project/eeg-database (accessed 8 May 2019).
 J. G. Proakis and D. G. Manolakis, “Digital Signal Processing,” Prentice Hall (4th Edition), 2006.
 M. Gentile and B. Straughan, “Bidispersive thermal convection,” International Journal of Heat and Mass Transfer Volume 114, November 2017, pages 837-840.
 J. Gama, “Knowledge Discovery from Data Streams,” Chapman & Hall/CRC, 2010.
 M. Last, A. Kandel, and H. Bunke, “Data Mining in Time Series Databases,” World Scientific Publishing Co. Pte. Ltd., 2004.
 A. Martone, G. Zazzaro, and L. Pavone, “A Feature Extraction Framework for Time Series Analysis. An Application for EEG Signal Processing for Epileptic Seizures Detection,” The 5th Int. Conf. on Big Data, Small Data, Linked Data and Open Data, March 2019, pp. 5-13.
 E. Keogh, K. Chakrabarti, M. J. Pazzani, and S. Mehrotra, “Dimensionality reduction for fast similarity search in large time series databases,” Knowledge and Info Sys 3(3), 2001, pp. 263–286.
 J. Lin, E. Keogh, S. Lonardi, and B. Chiu, “A symbolic representation of time series, with implications for streaming algorithms,” 8th ACM W on Research Issues in DM and KDD, 2003.
 G. Luo et al., “PLA - Piecewise Linear Approximation,” 31st Int Conf on Data Eng, 2015.
 I. Osorio, H. P. Zaveri, M. G. Frei, and S. Arthurs, “Epilepsy: The Intersection of Neurosciences, Biology, Mathematics, Engineering, and Physics,” in Rationales for Analogy between Earthquakes, Financial Crashes, and Epileptic Seizures, CRC Press, Taylor & Francis G, 2011.
 G. Zazzaro, F. M. Pisano, and G. Romano, “Bayesian Networks for Earthquake Magnitude Classification in a Early Warning System,” International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, Vol:6 No;4, 2012.
 M. Zaharia, “An Architecture for Fast and General Data Processing on Large Clusters,” PhD Dissertation, 2013.