Mr Yusuf Dinah

Research project: Developing Real-Time Seizure Detection Using Deep Learning Architectures


Epilepsy is a chronic illness of the brain whereby the patient cannot control the random firing of the brain's electrical activity, resulting in frequent unpredictable seizures. This disease affects over 50 million people worldwide (Ferreira, J. et al., 2009). It's the monitoring of the seizures that lead to the diagnosis of Epilepsy (Thomas et al., 2018).

The aim of the project is to design, develop and evaluate novel strategies to enhance the current methods of detecting seizures presented by epilepsy patients, using EEG data. Successful completion of the project involves developing novel state of the art architectures, in the following manner: 

  • A new 2D and CNN Bi-LSTM Architecture for EEG Analysis
  • A Legendre Memory Units-based Architecture for Real-time EEG Analysis
  • A Hybrid LSTM with Legendre Memory Units for Real-time EEG Analysis


  • 2nd/3rd Line Support 

Surrey and Borders partnership trust NHS

From November 2019 to July 2020

  • Junior Contract Developer

the digital parent company - Guildford

From September 2019 

To November 2019

  • Final Year Project and Charity Android Application

from July 2019 to September 2019

  • First Class BSc Software Engineering (Sandwich) Kingston University July 2019

BSc Hons Software Engineering (Final year Results) Module Programming IIIGrade AModule Dependable SystemsGrade AModule   Individual ProjectGrade AModule Internet SecurityGrade A

Areas of research interest

  • Artificial Intelligence
  • Convolutional neural networks
  • Bioinformatics


  • First Class in BSc Software Engineering

Funding or awards received

  • SEC Starting Bursary