Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

PTSD Case Detection with Boosting

Version 1 : Received: 29 March 2024 / Approved: 1 April 2024 / Online: 2 April 2024 (12:45:01 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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

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