Electroencephalography based delirium screening in acute supratentorial stroke
Year: 2024
Type: article
Study design: Observational Study
Abstract: Background Up to 25 percent of patients suffering from an acute stroke are diagnosed with delirium during the hospital stay, with older age increasing the risk. Generalized slowing in the electroencephalogram (EEG) supports the diagnosis of delirium. We examined the potential of single-channel EEG (DeltaScan®) as an easy-to-use device on intensive care units for detecting delirium. Our aim was to compare clinically established delirium diagnoses with bihemispheric EEG recordings and single-channel EEG to analyze the diagnostic accuracy of EEG-based diagnoses. Methods Within the first five days after stroke onset, patients received single-channel EEG DeltaScan® and a routine 21-channel EEG. The DeltaScan® analyzes right sided fronto-parietal EEG using a proprietary algorithm focusing on polymorphic delta activity (PDA). In routine EEG the power spectral density (PSD) in predefined frequency bands was analyzed based on 2-minute eyes-closed resting state segments. EEG-analyses were conducted in MNE (v1.3.1) in Python (3.10) and RStudio (v4.2.1). Results In 9 of 53 patients (52–90 years) delirium was diagnosed according to DSM-V criteria. Sensitivity of DeltaScan® was 44% (95% CI = 15.3–77.3%), while specificity was 71% (95% CI = 57–83%). We found patients with right hemispheric stroke having a higher probability to be false positive in DeltaScan® (p = 0.01). The 21-channel EEG based power analysis revealed frontal delta (sensitivity 0.89, 95% CI = 0.67-1; specificity 0.68, 95% CI = 0.54–0.8) as the best predictor of diagnosed delirium. Conclusions When EEG is used in clinical practice to support a delirium diagnosis in stroke patients, bihemispheric recordings are likely preferable over unilateral recordings. Slowing in the delta- or theta-frequency spectrum over the site of stroke may lead to false-positive results in single channel EEG based delirium scoring.
Source: BMC Neurology
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,890
OpenAlex ID: W4404339534