Usporedba konvolucijskih neuronskih mreža za klasifikaciju afektivnih poremećaja

master's thesis

• Title (english): Comparison of Convolutional Neural Networks for Affective Disorder Classification
• Author: Hana Ivandić
• Mentor: Mario Cifrek (mentor)
• Committee member: Mario Cifrek (predsjednik povjerenstva)
• Committee member: Tomislav Pribanić (član povjerenstva)
• Committee member: Alan Jović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2022-06-28, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Povratni depresivni poremećaj vrsta je afektivnog poremećaja. Rad se bavi klasifikacijom zapisa signala EEG-a dobivenih elektroencefalografijom u dvije klase: klasu depresivnih i klasu zdravih. Iz signala EEG-a izdvojeni su intervali kada pacijent miruje zatvorenih očiju te je provedena predobrada svih signala kako bi se iz njih uklonile smetnje i zadržali samo korisni dijelovi. Za opisani problem klasifikacije korišteno je nekoliko konvolucijskih neuronskih mreža različitih arhitektura i treniranih različitim postupcima. Rad uspoređuje njihove mjere vrednovanja i potrebna vremena treniranja međusobno, ali i s originalnim radovima iz kojih su preuzete arhitekture mreža. Korištena baza podataka dobivena je iz Klinike za psihijatriju Vrapče. Iz nje su izdvojeni zapisi signala EEG-a zdravih ljudi i pacijenata s povratnim depresivnim poremećajem s teškom trenutnom epizodom te je provedeno balansiranje ulaznog skupa podataka.

Abstract (english)

Recurrent depressive disorder is a type of affective disorder. This paper deals with the classification of EEG signal recordings obtained from electroencephalography in two classes: depressed and healthy. The resting state segments where the patients have their eyes closed were extracted from the EEG signals. The signals were also preprocessed in order to eliminate noise and keep only the relevant parts. Multiple convolutional neural networks with different architectures and training procedures were used to solve the classification problem. The paper compares their performance measures and training execution times both mutually and with the results from the original papers in which the architectures were proposed. The database was provided by the Vrapče Psychiatric Hospital. EEG signal recordings of the healthy people and the patients with recurrent depressive disorder with severe current episode were extracted from it and subsampled to form a balanced set.

• Keywords: afektivni poremećaji; elektroencefalografija; analiza neovisnih komponenti; konvolucijske neuronske mreže; klasifikacija
• Keywords (english): affective disorders; electroencephalography; independent component analysis; convolutional neural networks; classification
• Language: croatian
• URN:NBN: urn:nbn:hr:168:908761
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-14 08:56:32