PTSD Case Detection with Boosting
Author:
Nguyen Vu1, Phan Minh1, Wang Tiantian2, Norouzzadeh Payam3, Snir Eli2ORCID, Tutun Salih2, McKinney Brett4ORCID, Rahmani Bahareh5
Affiliation:
1. Mathematics and Computer Science Department, Fontbonne University, St. Louis, MO 63103, USA 2. Olin Business School, Washington University in Saint Louis, St. Louis, MO 63130, USA 3. School of Professional Studies, Saint Louis University, St. Louis, MO 63108, USA 4. Computer Science Department, University of Tulsa, Tulsa, OK 74104, USA 5. School of Medicine-AHEAD, Saint Louis University, St. Louis, MO 63104, USA
Abstract
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.
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