Nguyen, V.; Phan, M.; Wang, T.; Norouzzadeh, P.; Snir, E.; Tutun, S.; McKinney, B.; Rahmani, B. PTSD Case Detection with Boosting. Signals2024, 5, 508-515.
Nguyen, V.; Phan, M.; Wang, T.; Norouzzadeh, P.; Snir, E.; Tutun, S.; McKinney, B.; Rahmani, B. PTSD Case Detection with Boosting. Signals 2024, 5, 508-515.
Nguyen, V.; Phan, M.; Wang, T.; Norouzzadeh, P.; Snir, E.; Tutun, S.; McKinney, B.; Rahmani, B. PTSD Case Detection with Boosting. Signals2024, 5, 508-515.
Nguyen, V.; Phan, M.; Wang, T.; Norouzzadeh, P.; Snir, E.; Tutun, S.; McKinney, B.; Rahmani, B. PTSD Case Detection with Boosting. Signals 2024, 5, 508-515.
Abstract
In this project the EEG – electroencephalogram - channel(s) will be characterized to diagnose PTSD – Post-traumatic stress disorder – cases. For this aim, we applied boosting methods including a combination of K-mean and Support Vector Machine (SVM) models to find the feature weights to detect the PTSD cases. We classified 32 channels of 12 subjects including 6 PTSD and 6 healthy controls using a 6-mean classifier. The linear SVM found the weights of distinguished channels within each subject for each cluster. It was found that the significant SVM weights of F4, F8, and Pz are smaller in PTSD than in healthy subjects. This new method can be used as a tool to better understand the interaction of EEG signals and diagnosis.
Keywords
Linear Support Vector Machine; K-mean Clustering; PTSD; EEG
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
