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

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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