Cyber Security Based Application-Specific Integrated Circuit for Epileptic Seizure Prediction Using Convolutional Neural Network

 

Bala Dhandayuthapani V.1, Deepak Dudeja2, Sonia Duggal3, Sachin Sharma4, Anupriya Jain5, Piyush Kumar Pareek6

1Department of IT, College of Computing and Information Sciences, University of Technology and Applied Sciences, Shinas campus, Oman

2Professor, Department of Computer Science and Engineering, M.M. Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana, Ambala, Haryana

3Associate Professor, School of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, India

4Associate Professor, School of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, India

5Professor, School of Computer Applications, Manav Rachna International Institute of Research and Studies,

Faridabad, India

6Professor and Head Department of AIML and IPR Cell, Nitte Meenakshi Institute of Technology, Bengaluru, India

 

 

Emails: bala.veerasamy@utas.edu.om; deepak.dudeja@mmumullana.org; Sonia.sca@mriu.edu.in; sachin.sca@mriu.edu.in; Anupriya.sca@mriu.edu.in; piyush.kumar@nmit.ac.in,

 

Text Box: Abstract In the event of an epileptic attack, the Field-Programmable Gate Array (FPGA)-accelerated Convolutional Neural Network (CNN) model is paired with Electroencephalogram (EEG) acquisition equipment to produce a reliable production system that can be used in clinical medical diagnosis. Additionally, this study includes cybersecurity to protect both the epileptic patient’s data and the prediction system. Epilepsy is a frequent neurological disorder that manifests as recurrent seizures, a sign that indicates rapid intervention is necessary to minimize adverse events and improve patient health. The study provides a new real-time design for predicting epileptic seizures based on the Application-Specific Integrated Circuit (ASIC)-based Very Large-Scale Integration (VLSI) architecture. As a first step, EEG data from epilepsy patients were captured and pre-processed. Afterwards, faults and artefacts in the data were removed. Additionally, data was divided into short-time windows and then classified as either ictal, pre-seizure, or interictal. The CNN model was adapted for EEG signal analysis and then trained with categorized data. This technique is more effective and efficient for predicting epileptic seizures accurately, which is advantageous for patient monitoring and treatment. Additionally, cybersecurity measures were implemented to secure patient data and the prediction system. Corresponding Author: Piyush Kumar Pareek, Email:  piyush.kumar@nmit.ac.in,

Received: September 15, 2023 Revised: January 22, 2024 Accepted: June 09, 2024

 

Keywords: ASIC; CNN; Epilepsy; EEG data; FPGA; Seizure prediction; VLSI architecture