Fri, Sep 12, 2025
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Ethics code: IR.ISAAR.REC.1402.003
1- Department of Neuroscience, Institute for Cognitive Science Studies, Tehran, Iran
2- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
3- Department of Psychology, Kharazmi University, Tehran, Iran
4- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
2- Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran
3- Department of Psychology, Kharazmi University, Tehran, Iran
4- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
Abstract: (27 Views)
Introduction: Post-traumatic stress disorder (PTSD) is known as a severe psychological and emotional reaction to the experience of traumatic and adverse events such as war, life threats, natural disasters and personal violence and often leads to chronic mental and physical abnormalities. Studies have shown that executive function impairment is a characteristic of this disorder and cognitive assessments can be an important criterion in its diagnosis.
Methods: This study uses data from the CANTAB clinical SWM task to assess measures such as DTLR, CD, BE, WE, TF and DD at different stages. After normalization, the data are standardized and amplified by adding Gaussian noise. Then, a support vector machine model with a Gaussian kernel, which has a high ability to distinguish complex and nonlinear patterns due to its special characteristics, is trained.
results: The extracted features were effective in identifying complex differences in cognitive performance that characterize PTSD symptoms. The information collected from these features demonstrated the model's high ability to effectively simulate and analyze PTSD symptoms, as well as accurately distinguish between affected and non-affected groups.
Conclusion: The machine learning model used in this study has enabled the use of the CANTAB test as a neuropsychological tool for diagnosing PTSD. The accuracy of the model in this study reached 98%, indicating high power and confidence in correctly diagnosing individuals with this disorder.
Methods: This study uses data from the CANTAB clinical SWM task to assess measures such as DTLR, CD, BE, WE, TF and DD at different stages. After normalization, the data are standardized and amplified by adding Gaussian noise. Then, a support vector machine model with a Gaussian kernel, which has a high ability to distinguish complex and nonlinear patterns due to its special characteristics, is trained.
results: The extracted features were effective in identifying complex differences in cognitive performance that characterize PTSD symptoms. The information collected from these features demonstrated the model's high ability to effectively simulate and analyze PTSD symptoms, as well as accurately distinguish between affected and non-affected groups.
Conclusion: The machine learning model used in this study has enabled the use of the CANTAB test as a neuropsychological tool for diagnosing PTSD. The accuracy of the model in this study reached 98%, indicating high power and confidence in correctly diagnosing individuals with this disorder.
Type of Study: Research |
Received: 2025/02/25 | Accepted: 2025/09/11
Received: 2025/02/25 | Accepted: 2025/09/11
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