Protocol for the Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography (P-DROWS-E) study: a prospective observational study of delirium in elderly cardiac surgical patients. | LitMetric
Introduction: Delirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome.
Methods And Analysis: P-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1-2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time.
Ethics And Dissemination: P-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media.
Introduction: Emotion recognition using electroencephalography (EEG) is a key aspect of brain-computer interface research. Achieving precision requires effectively extracting and integrating both spatial and temporal features. However, many studies focus on a single dimension, neglecting the interplay and complementarity of multi-feature information, and the importance of fully integrating spatial and temporal dynamics to enhance performance.
Clinics of Otolaryngology, Hannover Medical School, Hearing Center Hannover (DHZ), Karl-Wiechert-Allee 3, 30625 Hannover, Germany; Institute of AudioNeuroTechnology (VIANNA) & Dept. of Experimental Otology, Hannover Medical School, Stadtfelddamm 34, 30625 Hannover, Germany. Electronic address:
Objective: We investigated auditory working-memory using behavioural measures and electroencephalography (EEG) in adult Cochlear Implant (CI) users with varying degrees of CI performance.
Methods: 24 adult CI listeners (age: M = 61.38, SD = 12.
Down syndrome (DS) is a common genetic condition affecting people worldwide. It involves cognitive disabilities for which there are no drug therapies. The Ts65Dn mouse model of DS shows cognitive impairment due to a reduction in neuron number and connectivity as well as excessive neuronal activity, as GABA antagonist treatment restores memory in these mice.
Vagal nerve stimulation (VNS) is a therapeutical option for the treatment of drug-resistant epileptic patients. The response to VNS varies from patient to patient and is difficult to predict. The proposed study is based on our previous work, identifying relative mean power in pre-implantation EEG as a reliable marker for VNS efficacy prediction in adult patients.
