Publications by authors named "B Westover"
Background: Postoperative delirium is the most common complication following surgery among older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, and loss of independence, as well as markedly increased health-care costs. Electroencephalography (EEG) spectral slowing has frequently been observed during episodes of delirium, whereas intraoperative frontal alpha power is associated with postoperative delirium. We sought to identify preoperative predictors that could identify individuals at high risk for postoperative delirium, which could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and postoperative delirium-related complications.
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- Atrial fibrillation (AF) is often undetected due to its asymptomatic nature, presenting a significant risk for stroke and heart failure, making early prediction and management essential.
- The study focused on analyzing 18,782 single-lead ECG recordings from 13,609 patients undergoing polysomnography (PSG) to identify individuals at high risk for developing AF, using both hand-crafted features and deep learning methods for prediction.
- By employing advanced feature extraction techniques, the researchers aimed to enhance AF detection using PSG data, ultimately improving patient outcomes through early intervention.
Background: Electroencephalography (EEG) is needed to diagnose nonconvulsive seizures. Prolonged nonconvulsive seizures are associated with neuronal injuries and deleterious clinical outcomes. However, it is uncertain whether the rapid identification of these seizures using point-of-care EEG (POC-EEG) can have a positive impact on clinical outcomes.
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- Atrial fibrillation (AF) is often unnoticed but poses significant risks for stroke and heart failure, making early detection and management vital, especially since many AF patients also suffer from obstructive sleep apnea (OSA).
- The study analyzed over 18,000 ECG recordings from patients at Massachusetts General Hospital to find indicators of AF by leveraging data from standard sleep assessments that included ECG monitoring.
- A deep learning approach was used to enhance the prediction model, extracting features from the ECG data to forecast individuals who are at high risk of developing AF in the future.