A direct classification approach to recognize stress levels in virtual reality therapy for patients with multiple sclerosis

Abstract

Multiple sclerosis (MS) is a chronic, debilitating, and often progressive inflammatory disease of the nervous system. The disease is highly stressful, which accelerates the risk of depression in people diagnosed with it and possibly exacerbates MS activity. One approach to reducing stress levels in such patients is to utilize virtual reality (VR). Using VR technology and recording physiological signals before and after displaying different environments to the individual, this article proposed a novel therapy procedure for improving stress levels. In the first phase, by distinguishing the stress level obtained from each environment watched by the patient, their corresponding labels are determined by two psychiatrists. Accordingly, the automated model is designed based on the analysis of VR scenes and can accurately classify MS patients' stress levels after watching the 3D environment. The proposed model consists of a fractal descriptor and SVM-RBF classifier to recognize VR scenes that can significantly reduce the stress level in MS patients. The accuracy of estimating MS patients' stress levels after watching different simulated VR environments is higher than 97%. By employing this method to classify VR scenes better and rehabilitate MS patients, it will be possible to significantly reduce their stress levels.

A Hybrid Approach for MS Diagnosis Through Nonlinear EEG Descriptors and Metaheuristic Optimized Classification Learning

“…The proposed method's feature selection has been carried out by getting inspiration from Rezaee and Zolfaghari method [ 40 ]. Their method was ant colony optimization (ACO), which was also considered in the present study to achieve the best subset of features, and the cost calculation in the fitness function was performed based on minimizing the classifier error.…”

“…Recent research has examined the application of new optimization algorithms to different learning methods and has been used in a variety of fields of medicine [ 39 ]. Using virtual reality technology and monitoring EEG signals before and after displaying diversified environments, Rezaee and Zolfaghari [ 40 ] developed a novel therapy approach for reducing stress levels in MS patients.…”

Deep learning‐based microarray cancer classification and ensemble gene selection approach

“…The ROC curves are representations of the true positive rate versus the false positive rate in the first approach. The area under the curve (AUC) may be determined using this illustration using Equation ( 2 ) [ 40 ]: …”

“…The ROC curves are representations of the true positive rate versus the false positive rate in the first approach. The area under the curve (AUC) may be determined using this illustration using Equation (2) [40]: $$$AUC=\underset{0{}}{\overset{1}{\int }}{T}_{1}\left({T}_{2}^{-1}(t)\right)\,dt\,{\Rightarrow}\,t\in (0,1)$$$ …”

Factors associated with the outcomes of a novel virtual reality therapy for military veterans with PTSD: Theory development using a mixed methods analysis

“…Multi-modular motion-assisted memory desensitization and reconsolidation therapy (3MDR) is an immersive psychological intervention first developed for military veterans with combat-related PTSD [ 12 ]. It is one of a number of immersive therapies which have recently appeared within the field of health care, with other examples including virtual reality interventions to enhance treatments for psychosis [ 13 ], improve neurocognitive functioning following brain injury [ 14 ], aid rehabilitation after stroke [ 15 ], and reduce stress in people with multiple sclerosis [ 16 ]. In 3MDR specifically, the person with PTSD walks on a treadmill towards a series of immersive, personally selected, images displayed on a wide screen and interacts with these with support from a therapist.…”