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Machine Learning-based Approaches for Post-Traumatic Stress Disorder Diagnosis using Video and EEG Sensors: A Review
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  • Alice OTHMANI ,
  • Bechir Brahem ,
  • Younes Haddou ,
  • Mustaqueem Khan
Université Paris-Est Créteil

Corresponding Author:[email protected]

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Bechir Brahem
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Younes Haddou
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Mustaqueem Khan
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According to the World Health Organization, approximately 6 in 100 people suffer from Post-traumatic stress disorder (PTSD) at some point in their lives. PTSD is a mental disorder with a set of symptoms observed in a person following exposure to a life-threatening event or witness a death. The primary characteristic of PTSD is the persistence over time of certain symptoms: flashbacks, traumatic nightmares, acute stress and symptoms of depression. In a stressful situation, the whole body goes into tension. This has an energy cost and affects the voice, breathing, daily gestures and the face and which are referred to in this survey paper as external symptoms.
The diagnosis of PTSD is based on the Diagnostic and Statistical Manual of Mental Disorders (also known by the acronym DSM) classification using standard questionnaires where the patient self-reports their condition based on their symptoms. Indeed, these questionnaires have several limitations and give an imprecise diagnosis.
Sensors and wearable based technologies can play a key role in improving the diagnosis, the prognosis and the assistance of PTSD. Recently, Computer-Aided Diagnosis (CAD) systems have been proposed for PTSD detection based on external symptoms and brain activity using video and EEG sensors. To the best of our knowledge, this is the first survey paper that gives a literature overview of machine learning based approaches for PTSD diagnosis using video and EEG sensors. In addition, a comparison between existing approaches and a discussion about the potential avenues for future PTSD research is provided.
15 Oct 2023Published in IEEE Sensors Journal volume 23 issue 20 on pages 24135-24151. 10.1109/JSEN.2023.3312172