PTSD Case Detection with Boosting

Bibliographic Details
Title: PTSD Case Detection with Boosting
Authors: Vu Nguyen, Minh Phan, Tiantian Wang, Payam Norouzzadeh, Eli Snir, Salih Tutun, Brett McKinney, Bahareh Rahmani
Source: Signals, Vol 5, Iss 3, Pp 508-515 (2024)
Publisher Information: MDPI AG, 2024.
Publication Year: 2024
Collection: LCC:Applied mathematics. Quantitative methods
Subject Terms: linear support vector machine, k-means clustering, PTSD, EEG, Applied mathematics. Quantitative methods, T57-57.97
More Details: In this project, the electroencephalogram (EEG) channel(s) is used to better characterize post-traumatic stress disorder (PTSD). For this aim, we applied boosting methods along with a combination of k-means and Support Vector Machine (SVM) models to find the diagnostic channels of PTSD cases and healthy subjects. We grouped 32 channels and 12 subjects (6 PTSD and 6 healthy controls) using k-means. Channels of the brain are grouped by the k-means clustering method to find the most similar part of the brain. This approach uses SVM by performing classification based on cluster classes are been mapped to EEG channels. This mapping uses information across all samples without the bias of using the outcome variable. The linear SVM found weights that distinguished channels within each subject for each cluster to compare the PTSD cases and healthy controls’ channel weights. It was found that the significant SVM weights of F4, F8, and Pz were smaller in subjects with PTSD than in healthy subjects. This new method can be used as a tool to better understand the relationship between EEG signals and diagnosis.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2624-6120
Relation: https://www.mdpi.com/2624-6120/5/3/27; https://doaj.org/toc/2624-6120
DOI: 10.3390/signals5030027
Access URL: https://doaj.org/article/963dd16af23547e59757d767384cb34d
Accession Number: edsdoj.963dd16af23547e59757d767384cb34d
Database: Directory of Open Access Journals
More Details
ISSN:26246120
DOI:10.3390/signals5030027
Published in:Signals
Language:English