Home > Academic theses (ETDs) > Bachelor's theses > Electroencephalogram (EEG) and machine learning based classification of depression: unveiling hidden patterns for early detection
Original title:
Electroencephalogram (EEG) and machine learning based classification of depression: unveiling hidden patterns for early detection
Translated title:
Electroencephalogram (EEG) and machine learning based classification of depression: unveiling hidden patterns for early detection
Authors:
Jurkechová, Adriana ; Malik, Aamir Saeed (referee) ; Zaheer, Muhammad Asad (advisor) Document type: Bachelor's theses
Year:
2024
Language:
eng Publisher:
Vysoké učení technické v Brně. Fakulta informačních technologií Abstract:
[eng][cze]
Táto práca sa zaoberá predspracovaním EEG signálov, extrakciou vlastností a klasifikáciou pacientov s depresiou a zdravou kontrolnou skupinou. Na klasifikáciu bolo zväžených a ohodnotených 5 modelov strojového učenia. Získané poznatky potvrdzujú výsledky z predchádzajúcich výskumov a poukazujú na dôležitosť veľkého a diverzného datasetu. Táto práca pracuje s verejne dostupným datasetom.
This work deals with the pre-processing EEG signals, extraction of the features and classifying depressed patients and healthy control group. For classification, 5 different machine learning models were considered and evaluated. Findings confirm results from prior research and show the importance of a large, diverse dataset. This work utilises a public dataset.
Keywords:
depresia; EEG; extracia vlastností; klasifikácia depresie; predspracovanie signálov; strojové učenie; depression; depression classification; EEG; feature extraction; machine learning classification; signal pre-processing
Institution: Brno University of Technology
(web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library. Original record: https://hdl.handle.net/11012/246895