The week's pick
Random Articles
Reseach Article
Long Short-Term Memory (LSTM) based Epileptic Seizure Recognition
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 22 |
| Year of Publication: 2022 |
| Authors: Sunil Kumar D.S., Mamatha Mallesh, Bharath K.N., Susmitha B.C., Kiran |
10.5120/ijca2022922260
|
Sunil Kumar D.S., Mamatha Mallesh, Bharath K.N., Susmitha B.C., Kiran . Long Short-Term Memory (LSTM) based Epileptic Seizure Recognition. International Journal of Computer Applications. 184, 22 ( Jul 2022), 23-29. DOI=10.5120/ijca2022922260
Abstract
Epilepsy is the second most common brain disorder after migraine; automatic detection of epileptic seizures can considerably improve the patients’ quality of life. Current Electroencephalogram (EEG)-based seizure detection systems encounter many challenges in real-life situations; EEG data are prone to numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address this challenge, we propose a deep learning-based approach that learns the discriminative EEG features of epileptic seizures and to distinguish between the different types of patient recordings. More specifically, we aim to tackle this issue by using a Long Short-Term Memory network, and explore the capabilities of this model.
References
- G. Rogers, “Epilepsy: the facts,” Primary Health Care Research & Development, vol. 11, no. 4, p. 413, 2010.
- U. R. Acharya, S. V. Sree, G. Swapna, R. J. Martis, and J. S. Suri, “Automated EEG analysis of epilepsy: a review,” Knowledge-Based Systems, vol. 45, pp. 147–165, 2013.
- F. Mormann, R. G. Andrzejak, C. E. Elger, and K. Lehnertz, “Seizure prediction: the long and winding road,” Brain, vol. 130, no. 2, pp. 314– 333, 2006.
- R. Meier, H. Dittrich, A. Schulze-Bonhage, and A. Aertsen, “Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns,” Journal of Clinical Neurophysiology, vol. 25, no. 3, pp. 119– 131, 2008.
- G. R. Minasyan, J. B. Chatten, M. J. Chatten, and R. N. Harner, “Patientspecific early seizure detection from scalp EEG,” Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society, vol. 27, no. 3, p. 163, 2010.
- A. G. Correa, E. Laciar, H. Patino, and M. Valentinuzzi, “Artifact ˜ removal from EEG signals using adaptive filters in cascade,” in Journal of Physics: Conference Series, vol. 90, no. 1. IOP Publishing, 2007, p. 012081.
- A. M. Chan, F. T. Sun, E. H. Boto, and B. M. Wingeier, “Automated seizure onset detection for accurate onset time determination in intracranial EEG,” Clinical Neurophysiology, vol. 119, no. 12, pp. 2687–2696, 2008.
- A. Aarabi, R. Fazel-Rezai, and Y. Aghakhani, “A fuzzy rule-based system for epileptic seizure detection in intracranial EEG,” Clinical Neurophysiology, vol. 120, no. 9, pp. 1648–1657, 2009.
- J. J. Niederhauser, R. Esteller, J. Echauz, G. Vachtsevanos, and B. Litt, “Detection of seizure precursors from depth EEG using a sign periodogram transform,” IEEE Transactions on Biomedical Engineering, vol. 50, no. 4, pp. 449–458, 2003.
- I. Guler and E. D. ¨ Ubeyli, “Adaptive neuro-fuzzy inference system for ¨ classification of EEG signals using wavelet coefficients,” Journal of neuroscience methods, vol. 148, no. 2, pp. 113–121, 2005.
- A. T. Tzallas, M. G. Tsipouras, and D. I. Fotiadis, “Automatic seizure detection based on time-frequency analysis and artificial neural networks,” Computational Intelligence and Neuroscience, vol. 2007, 2007.
- B. Abibullaev, H. D. Seo, and M. S. Kim, “Epileptic spike detection using continuous wavelet transforms and artificial neural networks,” International journal of wavelets, multiresolution and information processing, vol. 8, no. 01, pp. 33–48, 2010.
- J. Mitra, J. R. Glover, P. Y. Ktonas, A. T. Kumar, A. Mukherjee, N. B. Karayiannis, J. D. Frost Jr, R. A. Hrachovy, and E. M. Mizrahi, “A multi-stage system for the automated detection of epileptic seizures in neonatal EEG,” Journal of clinical neurophysiology: official publication of the American Electroencephalographic Society, vol. 26, no. 4, p. 218, 2009.
- K. Abualsaud, M. Mahmuddin, M. Saleh, and A. Mohamed, “Ensemble classifier for epileptic seizure detection for imperfect EEG data,” The Scientific World Journal, vol. 2015, 2015.
- Zeng, Hong, Jiaming Zhang, Wael Zakaria, Fabio Babiloni, Borghini Gianluca, Xiufeng Li, and Wanzeng Kong. "InstanceEasyTL: an improved transfer-learning method for EEG-based cross-subject fatigue detection." Sensors 20, no. 24 (2020): 7251.
- Yıldırım, Özal, Ulas Baran Baloglu, and U. Rajendra Acharya. "A deep convolutional neural network model for automated identification of abnormal EEG signals." Neural Computing and Applications 32, no. 20 (2020): 15857-15868.
- RaviPrakash, Harish, Milena Korostenskaja, Eduardo M. Castillo, Ki H. Lee, Christine M. Salinas, James Baumgartner, Syed M. Anwar, Concetto Spampinato, and Ulas Bagci. "Deep Learning provides exceptional accuracy to ECoG-based Functional Language Mapping for epilepsy surgery." Frontiers in neuroscience (2020): 409.
Index Terms
Keywords