2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 13 , Issue 1 , PP: 234-250, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Bala Dhandayuthapani V. 1 * , Deepak Dudeja 2 , Sonia Duggal 3 , Sachin Sharma 4 , Anupriya Jain 5 , Piyush Kumar Pareek 6
Doi: https://doi.org/10.54216/JISIoT.130117
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.
ASIC , CNN , Epilepsy , EEG data , FPGA , Seizure prediction , VLSI architecture
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