IMPLEMENT OF DEEP NEURAL NETWORK FOR REPRESENTING THE PROPERTIES OF THE PROBLEM OF EPILEPSY SPIKES DETECTION IN ELECTROENCEPHALOGRAPHY (EEG) DIAGNOSTIC

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Authors

Keywords:

Deep learning; Neural network; Machine learning; Bioinformatics; Biomedical signal processing; Epileptic spike; EEG.

Abstract

Deep Neural Network (DNN) is the machine learning algorithm that is higher development from Artificial Neural Network (ANN) for learning present the object model in multi-layers. The paper demonstrates the method for detecting and analyzing automatically epilepsy spikes from the huge data collected from electroencephalography. This helps the doctor determine the area of the brain that causes epilepsy. Millions of manually analyzed samples are "re-trained" to figure out the continuous spikes emanating from the related brain area. The author also proposes to build the systems in which some trial deep learning models are used, which assists physicians in early diagnosis and treatment.

References

[1]. Manyika J, Chui M, Brown B et al. “Big data: The next frontier for innovation, competition, and productivity”, 2011.

[2]. “Deep Mind Health”. Google DeepMind. https://www.deepmind.com/health, 2016.

[3]. Larranaga P, Calvo B, Santana R et al. “Machine learning in bioinformatics”. Briefings in bioinformatics 2006; 7(1): 86-112.

[4]. Schmidhuber J. “Deep learning in neural networks: An overview”. Neural networks 2015; 61:85- 117.

[5]. Leung MK, Delong A, Alipanahi B et al. “Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets”, 2016.

[6]. Cheng-Wen Ko and Hsiao-Wen Chung. “Automatic spike detection via an artificial neural network using raw eeg data: effects of data preparation and implications in the limitations of online recognition”. Clinical neurophysiology, 111(3): 477–481, 2000.

[7]. A page from: Eric Hargreaves'Page O'Neuroplasticity (last updated July 2006). “Kindling: a model of focal epilepsy”.

[8]. Yung-Chun Liu, Chou-Ching K Lin, Jing-Jane Tsai, and Yung-Nien Sun. “Model-based spike detection of epileptic eeg data”. Sensors, 13(9):12536-12547, 2013.

[9]. Watanabe Y., Johnson RS., Butler LS., Binder DK., Spiegelman BM. Papaioannou VE., McNamara JO. (1996). “Null mutation of c-fos impairs structural and functional plasticities in the kindling model of epilepsy”. Journal of Neuroscience, 16 3827-36.

[10]. Racine, R.J. and Burnham, W.M. (1984). “The Kindling model”. In P.A. Schwartzkroin and H. Wheal (Eds) Electrophysiology of Epilepsy pp153-171.

[11]. Alexander Rosenberg Johansen, Jing Jin, Tomasz Maszczyk, Justin Dauwels, Sydney S Cash, and M Brandon Westover. “Epileptiform spike detection via convolutional neural networks”. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, pages 754-758. IEEE, 2016.

[12]. Christophe Andrieu, Nando De Freitas, Arnaud Doucet, and Michael I Jordan. “An introduction to mcmc for machine learning”. Machine learning, 50(1-2): 5-43, 2003.

[13]. He Sheng Liu, Tong Zhang, and Fu Sheng Yang. “A multistage, multimethod approach for automatic detection and classification of epileptiform EEG”. Biomedical Engineering, IEEE Transactions on, 49(12): 1557-1566, 2002.

[14]. Li Y, Shi W, Wasserman WW. “Genome-Wide Prediction of cis-Regulatory Regions Using Supervised Deep Learning Methods”. bioRxiv, 2016:041616.

Published

14-12-2020

How to Cite

Xuyến. “IMPLEMENT OF DEEP NEURAL NETWORK FOR REPRESENTING THE PROPERTIES OF THE PROBLEM OF EPILEPSY SPIKES DETECTION IN ELECTROENCEPHALOGRAPHY (EEG) DIAGNOSTIC”. Journal of Military Science and Technology, no. 70, Dec. 2020, pp. 77-84, https://online.jmst.info/index.php/jmst/article/view/113.

Issue

Section

Research Articles