E-ISSN 0976-2833 | ISSN 0975-3583
 

Research Article 


Proficient RO-RNN Learning Model for Seizure Prediction Systems

Banu Priya Prathaban, Ramachandran Balasubramanian.

Abstract
According to WHO, world widely around 65 million people are affected by epilepsy. Prediction of such a lifethreatening
neurological disease is of high importance. Predictability of seizures uplifts the patientís life and
wellbeing. This paper presents the application of machine learning in the prediction of epileptic seizures. In this
work, we used Regularized and Optimized Recurrent Neural Network (RO-RNN). The aim of this work is to
investigate the application of bidirectional long short-term memory (LSTM) networks for epileptic seizure
prediction. A Weight Dropped (WD) method is used for regularizing the LSTM model and an Averaged
Stochastic Gradient Descent (ASGD) is used for optimizing the LSTM model. Regularization and optimization
are deployed with the deep neural network architecture to accelerate the convergence rate and to reduce the
complexity of the proposed non-linear model. The proposed model is evaluated using two diverse public
databases such as traditional CHB-MIT and recent NINC respectively. Also, a private real time SRM database is
used for the assessment of the proposed computer-aided seizure prediction approach. Empirical results on 200
recordings outperforms the state-of-art approaches with an accuracy score of 0.91, sensitivity score of 0.89 and
false prediction rate of 0.12 FP/h. Experimental results prove that the proposed seizure prediction approach is a
promising one for accurate real-time prediction of epilepsy using scalp EEG data.

Key words: Averaged Stochastic Gradient Descent; bidirectional long short-term memory; computer-aided seizure prediction; deep neural network architecture; Regularized and Optimized Recurrent Neural Network; Weight Dropped.


 
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Pubmed Style

Banu Priya Prathaban, Ramachandran Balasubramanian. Proficient RO-RNN Learning Model for Seizure Prediction Systems. J Cardiovasc. Dis. Res.. 2021; 12(6): 161-166. doi:10.31838/jcdr.2021.12.06.22


Web Style

Banu Priya Prathaban, Ramachandran Balasubramanian. Proficient RO-RNN Learning Model for Seizure Prediction Systems. http://www.jcdronline.org/?mno=130782 [Access: January 16, 2022]. doi:10.31838/jcdr.2021.12.06.22


AMA (American Medical Association) Style

Banu Priya Prathaban, Ramachandran Balasubramanian. Proficient RO-RNN Learning Model for Seizure Prediction Systems. J Cardiovasc. Dis. Res.. 2021; 12(6): 161-166. doi:10.31838/jcdr.2021.12.06.22



Vancouver/ICMJE Style

Banu Priya Prathaban, Ramachandran Balasubramanian. Proficient RO-RNN Learning Model for Seizure Prediction Systems. J Cardiovasc. Dis. Res.. (2021), [cited January 16, 2022]; 12(6): 161-166. doi:10.31838/jcdr.2021.12.06.22



Harvard Style

Banu Priya Prathaban, Ramachandran Balasubramanian (2021) Proficient RO-RNN Learning Model for Seizure Prediction Systems. J Cardiovasc. Dis. Res., 12 (6), 161-166. doi:10.31838/jcdr.2021.12.06.22



Turabian Style

Banu Priya Prathaban, Ramachandran Balasubramanian. 2021. Proficient RO-RNN Learning Model for Seizure Prediction Systems. Journal of Cardiovascular Disease Research, 12 (6), 161-166. doi:10.31838/jcdr.2021.12.06.22



Chicago Style

Banu Priya Prathaban, Ramachandran Balasubramanian. "Proficient RO-RNN Learning Model for Seizure Prediction Systems." Journal of Cardiovascular Disease Research 12 (2021), 161-166. doi:10.31838/jcdr.2021.12.06.22



MLA (The Modern Language Association) Style

Banu Priya Prathaban, Ramachandran Balasubramanian. "Proficient RO-RNN Learning Model for Seizure Prediction Systems." Journal of Cardiovascular Disease Research 12.6 (2021), 161-166. Print. doi:10.31838/jcdr.2021.12.06.22



APA (American Psychological Association) Style

Banu Priya Prathaban, Ramachandran Balasubramanian (2021) Proficient RO-RNN Learning Model for Seizure Prediction Systems. Journal of Cardiovascular Disease Research, 12 (6), 161-166. doi:10.31838/jcdr.2021.12.06.22





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