Efficacy of Smart EEG Monitoring Amidst the COVID-19 Pandemic

Abstract

Ever since the COVID-19 pandemic has majorly altered diagnosis and prognosis practices, the need for telemedicine and mobile/electronic health has never been more appreciated. Drastic complications of the pandemic such as burdens on the social and employment status resulting from extended quarantine and physical distancing, has also negatively impacted mental health. Doctors and healthcare workers have seen more than just the lungs affected by COVID-19. Neurological complications including stroke, headache, and seizures have been reported for populations of patients. Most mental conditions can be detected using the Electroencephalogram (EEG) signal. Brain disorders, neurodegenerative diseases, seizure/epilepsy, sleep/fatigue, stress, and depression have certain characteristics in the EEG wave, which clearly differentiate them from normal conditions. Smartphone apps analyzing the EEG signal have been introduced in the market. However, the efficacy of such apps has not been thoroughly investigated. Factors and their inter-relationships impacting efficacy can be studied through a causal model. This short communications/perspective paper outlines the initial premises of a system dynamics approach to assess the efficacy of smart EEG monitoring apps amid the pandemic, that could be revolutionary for patient well-being and care policies.

Keywords

• EEG
• smartphone app
• COVID-19 pandemic
• causal model
• systems engineering
• system dynamics