Machine Learning-based Approaches for Post-Traumatic Stress Disorder
Diagnosis using Video and EEG Sensors: A Review
Abstract
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.