Open AccessUsing Deep Learning algorithms to detect the success or failure of the Electroconvulsive Therapy (ECT) sessions
Author(s) -
Usef Faghihi,
Cyrus kalantarpour
Publication year - 2021
Publication title -
proceedings of the ... international florida artificial intelligence research society conference
Language(s) - English
Resource type - Journals
eISSN - 2334-0762
pISSN - 2334-0754
DOI - 10.32473/flairs.v34i1.128367
Subject(s) - electroconvulsive therapy , major depressive disorder , electroencephalography , mental health , depression (economics) , health professionals , session (web analytics) , psychology , psychiatry , magnetic resonance imaging , mental healthcare , health care , medicine , computer science , cognition , radiology , world wide web , economics , macroeconomics , economic growth
Major Depression Disorder (MDD) is a big problem in our society. MDD can cause suicide and take families apart. When treatment with medications fail, mental healthcare professionals, use Electroconvulsive Therapy (ECT) to treat patients with 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. So far, to our knowledge, researchers have used multi-parametric magnetic resonance imaging (MRI) techniques combined with statistical methods and/or linear machine learning algorithms to predict patients’ responses to ECT. However, the aforementioned methods are very expensive and time-consuming. In this study, we will be using Deep learning algorithms to detect the effectiveness of ECT sessions based on the EEG.