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A Proposal to Automate Seizure Detection based on a Comparative Study of EEG Signal Analysis

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Hrishikesh Telang, Shreya More, Yatri Modi, Ruhina Karani
10.5120/ijca2017915637

Hrishikesh Telang, Shreya More, Yatri Modi and Ruhina Karani. A Proposal to Automate Seizure Detection based on a Comparative Study of EEG Signal Analysis. International Journal of Computer Applications 176(7):22-27, October 2017. BibTeX

@article{10.5120/ijca2017915637,
	author = {Hrishikesh Telang and Shreya More and Yatri Modi and Ruhina Karani},
	title = {A Proposal to Automate Seizure Detection based on a Comparative Study of EEG Signal Analysis},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2017},
	volume = {176},
	number = {7},
	month = {Oct},
	year = {2017},
	issn = {0975-8887},
	pages = {22-27},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume176/number7/28567-2017915637},
	doi = {10.5120/ijca2017915637},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Epilepsy is a chronic neurological disorder which is characterized by recurrent and sudden seizures. People with epilepsy suffer from multiple types of seizures and Electroencephalography is an important clinical tool for diagnosing, monitoring and managing neurological disorders related to epilepsy. EEG signals are most often used to diagnose epilepsy, as seizures cause anomalies in EEG readings. In today’s world where adult life expectancy is rising and humans are living longer than ever before, the healthcare system generates vast amounts of data, including EEG signals. This paper examines the prospects and challenges faced in utilizing this data in order to optimize seizure detection in order to improve the patients’ quality of life. This paper also explores how Machine Learning can be applied to extract features and analyze the EEG signals and propose methods to achieve high classification accuracy.

References

  1. Mohammad-Parsa Hosseini, Mohammad R. Nazem-Zadeh, Dario Pompili, Hamid Soltanian-Zadeh, “Statistical validation of automatic methods for hippocampus segmentation in MR images of epileptic patients”, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014.
  2. Mohammad-Parsa Hosseini, Hamid Soltanian-Zadeh, Kost Elisevich, Dario Pompili, “Cloud-based deep learning of big EEG data for epileptic seizure prediction”, IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016.
  3. Ales Prochazka, Jaromir Kukal, Oldrich Vysata, “Wavelet transform use for feature extraction and EEG signal segments classification”, 3rd International Symposium on Communications, Control and Signal Processing, 2008. ISCCSP 2008.
  4. Tulga Kalayci, Ozcan Ozdamar, Nurgiin Erdol, “The use of wavelet transform as a preprocessor for the neural network detection of EEG spikes”, Southeastcon '94. Creative Technology Transfer - A Global Affair., Proceedings of the 1994 IEEE.
  5. Md. Khayrul Bashar, Faruque Reza, Zamzuri Idris, Hiroaki Yoshida, “ Epileptic seizure classification from intracranial EEG signals: A comparative study EEG -based seizure classification”, IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2016.
  6. A. Maragakis and A. Rosengarten, “Seizure Detection with Single-Channel On-Scalp EEG using SVM-Ensembles,” International Mathematics Research Notices, Vol. 2015, Article ID rnn000, 8 pages. doi:10.1093/imrn/rnn000, 2015.
  7. Sabrina Ammar, Mohamed Senouci,”Seizure detection with single-channel EEG using Extreme Learning Machine”, 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), 2016.
  8. Wijesinghe, L.P, Wickramasuriya, D.S, Pasqual, A.A, “A generalized preprocessing and feature extraction platform for scalp EEG signals on FPGA”, IEEE Conference on Biomedical Engineering and Sciences (IECBES), 2014.
  9. Sanjay Gupta, Harvinder Singh, “Preprocessing EEG signals for direct human-system interface”, IEEE International Joint Symposia on Intelligence and Systems, 1996.
  10. Tulga Kalayci, Ozcan Ozdamar, “Wavelet preprocessing for automated neural network detection of EEG spikes”, IEEE Engineering in Medicine and Biology Magazine ( Volume: 14, Issue: 2, Mar/Apr 1995 ).
  11. R R Sreekrishna, Saif Nalband, A. Amalin Prince, “Real time cascaded moving average filter for detrending of electroencephalogram signals”, International Conference on Communication and Signal Processing (ICCSP), 2016.
  12. Ataee, P., Avanaki, A.N., Shariatpanahi, H.F., Khoee, S.M, “ Ranking features of wavelet-decomposed EEG based on significance in epileptic seizure prediction”, 14th European Signal Processing Conference, IEEE, 2006.
  13. Abhijit Bhattacharyya, Ram Bilas Pachori, “A Multivariate Approach for Patient-Specific EEG Seizure Detection Using Empirical Wavelet Transform”, IEEE Transactions on Biomedical Engineering ( Volume: 64, Issue: 9, Sept. 2017 ).
  14. Zhang, Y., Liu, B., Ji, X. et al., “Classification of EEG signals based on Autoregressive model and Wavelet Packet Decomposition”, Neural Processing Letters,April 2017, Volume 45, Issue 2, pp 365–378.
  15. A. Garces, E. Correa, E. Laciar, D P. Hector, E V. Maximo, "An Automatic Sleep-Stage Classifier Using Electroencephalographic Signals", International journal of Medical Science and technology, pp. 13-21, 2008.
  16. Guang-Bin Huang, Qin-Yu Zhu, and Chee-Kheong Siew, “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks”, Proceedings. 2004 IEEE International Joint Conference on Neural Networks, 2004.
  17. [ONLINE] Andrew L. Maas, Awni Y. Hannun, Andrew Y. Ng, “Rectifier Nonlinearities Improve Neural Network Acoustic Models “, Computer Science Department, Stanford University, CA 94305 USA - http://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf .
  18. Alexander Rosenberg Johansen, Jing Jin, Tomasz Maszczyk, Justin Dauwels, Sydney S. Cash, M. Brandon Westover, “Epileptiform spike detection via convolutional neural networks”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016.
  19. L. Vidyaratne, A. Glandon, M. Alam, K. M. Iftekharuddin, “Deep recurrent neural network for seizure detection”, International Joint Conference on Neural Networks (IJCNN), 2016.

Keywords

EEG signal analysis, Epileptic Seizure Detection, Machine Learning, Feature Extraction, Wavelet Transform, Signal Preprocessing, Signal Classification, Bidirectional Neural Networks, Auto Regressive model, Approximate Entropy, Wavelet Packet Decomposition, Extreme Learning Machines.