Abstract
We used quantified electroencephalography (qEEG) to define the features of encephalopathy in patients released from the intensive care unit after severe illness from COVID-19. Artifact-free 120–300 s epoch lengths were visually identified and divided into 1 s windows with 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel and window, the power spectrum was calculated and used to compute the area for delta (0–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands. Furthermore, Shannon’s spectral entropy (SSE) and synchronization by Pearson’s correlation coefficient (ρ) were computed; cases of patients diagnosed with either infectious toxic encephalopathy (ENC) or post-cardiorespiratory arrest (CRA) encephalopathy were used for comparison. Visual inspection of EEGs of COVID patients showed a near-physiological pattern with scarce anomalies. The distribution of EEG bands was different for the three groups, with COVID midway between distributions of ENC and CRA; specifically, temporal lobes showed different distribution for EEG bands in COVID patients. Besides, SSE was higher and hemispheric connectivity lower for COVID. We objectively identified some numerical EEG features in severely ill COVID patients that can allow positive diagnosis of this encephalopathy.
Highlights
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) causes an acute, highly lethal disease, COVID-19
We used quantified electroencephalography to define the features of encephalopathy in patients released from the intensive care unit after severe illness from COVID-19
Shannon’s spectral entropy (SSE) and synchronization by Pearson’s correlation coefficient (ρ) were computed; cases of patients diagnosed with either infectious toxic encephalopathy (ENC) or post-cardiorespiratory arrest (CRA) encephalopathy were used for comparison
Summary
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) causes an acute, highly lethal disease, COVID-19. This disease was first detected in December 2019 in China and rapidly spread around the world. The development of mathematical analysis tools for bioelectric signals, commonly known as quantified EEG (qEEG), has introduced elements of objectivity into the analysis of EEG records [16,17]. With this goal in mind, we developed a qEEG using classical mathematical methods, but in a neurophysiologically and clinically oriented fashion. We started from the assumption that EEG is based in a homeostatic system [18,19], in order to establish an approximate direct relationship between variations in numerical magnitude and the underlying anatomo-functional system
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