Titulo Estágio
Epileptic seizure detection and prediction based on topographic maps and deep machine learning (EPI-DEEP)
Áreas de especialidade
Sistemas Inteligentes
Local do Estágio
DEI-FCTUCI
Enquadramento
Biological human neocortex is organized in a modular hierarchy of modules that overtime learn to represent observations based on their regularities . Aiming to model this behavior a class of machine learning methods called deep machine learning emerged. It is recognized that deep machine learning methods are suited to successful capture spatial-temporal dependencies. An option to improve the quality of life of epileptic patients encompasses the development of algorithms that could warn the patient of an impending seizure. The performance of the algorithms developed so far is not adequate for a real clinical application. Thus, new approaches are needed. An unexplored aspect is the long-term analysis of spatial-temporal patterns obtained from topographic maps and that are expected to bring encouraging results. Topographic maps are spatial representations of a given electroencephalogram feature over the scalp, and that may change over-time, exhibiting the cerebral dynamics.
Objetivo
In this master dissertation it is aimed to apply deep machine learning methods to identify patterns related to seizure events based on topographic maps. Topographic maps will be developed over-time and deep machine learning approaches, such as Convolutional Neural Networks (CNN) and Deep Belief Networks (DBNs) will be considered to capture the cerebral dynamics of the epileptic brain.
More info on:
-http://web.eecs.utk.edu/~itamar/Papers/DML_Arel_2010.pdf
Plano de Trabalhos - Semestre 1
1. Literature review
• Objective: In the first phase the student should understand the problem of seizure detection and prediction by computational intelligence methods, and understand the main deep-learning approaches.
• Start: September 2015
• End: November 2015
• Expected Result: A report describing the actual formulations of seizure prediction by computational intelligence methods, and the main deep learning approaches suitable for the epileptic seizure detection and prediction problem.
2a. Implementation of deep learning methods
• Objective: Development of a toolbox for seizure detection and prediction by deep learning and topographic maps.
• Start: October 2015
• End: Dezember 2015
• Expected Result: A toolbox that should enable the development of detectors/predictors based on topographic maps and deep-learning.
Plano de Trabalhos - Semestre 2
2b. Implementation of deep learning methods
• Objective: Development of a toolbox for seizure detection and prediction by deep learning and topographic maps.
• Start: January 2016
• End: March 2016
• Expected Result: A toolbox that should enable the development of detectors/predictors based on topographic maps and deep-learning.
3. Analysis of the algorithms in a large set of patients
• Objective: Using the developed toolbox, predictors for 100 patients from the EPILEPSIAE database will be developed.
• Start: February 2016
• End: June 2016
• Expected Result: A report describing the results obtained in the 100 patients considered.
4. Thesis writing
• Start: May 2016
• End: July 2012
Condições
The work is supposed to be developed on the Adaptive Computation Group Laboratories-CISUC, under normal logistic conditions.
Observações
Depending on the work quality, publications in international conferences may occur.
Orientador
César Alexandre Domingues Teixeira
cteixei@dei.uc.pt 📩