Using causal probabilistic fuzzy logic (PFL) rules integrated with Deep learning algorithms (DLs) to analyze Electroencephalography (EEGs)

Major Depression Disorder (MDD) is a big problem in our society. About 8% of Canadians may suffer from depressions in their life. Major depression can cause suicide and take families apart. Canadian governments spend more than $51 billion a year in the mental health sector. When treatment with medications fail, mental healthcare professionals, use Electroconvulsive Therapy (ECT) to treat patients with Major Depression Disorders (MDD). During an ECT session, electroencephalogram (EEG) signals let the mental healthcare professionals record patients’ brain activities which are helpful to decide whether the treatment was successful. However, there is no standard way to know how and with what intensity a healthcare professional needs to apply electroshock to treat patients with MDD. In this work, we will use non-classical logics such as probabilistic fuzzy logic and deep learning algorithms in order to find the ECT features resulting in successful ECTs.

Faculty Supervisor:

Usef Faghihi

Student:

Partner:

Centre intégré universitaire de santé et de services sociaux de la Mauricie-et-du-Centre-du-Québec

Discipline:

Computer science

Sector:

Health and Related Sciences & Technology; Artificial Intelligence

University:

Université du Québec à Trois-Rivières

Program: