Eeg-Derived Estimators of Present and Future Cognitive Performance

Previous electroencephalography (EEG)-based fatigue-related research primarily focused on the association between concurrent cognitive performance and time-locked physiology. The goal of this study was to investigate the capability of EEG to assess the impact of fatigue on both present and future cognitive performance during a 20-min sustained attention task, the 3-choice active vigilance task (3CVT), that requires subjects to discriminate one primary target from two secondary non-target geometric shapes. The current study demonstrated the ability of EEG to estimate not only present, but also future cognitive performance, utilizing a single, combined reaction time (RT), and accuracy performance metric. The correlations between observed and estimated performance, for both present and future performance, were strong (up to 0.89 and 0.79, respectively). The models were able to consistently estimate “unacceptable” performance throughout the entire 3CVT, i.e., excessively missed responses and/or slow RTs, while acceptable performance was recognized less accurately later in the task. The developed models were trained on a relatively large dataset (n = 50 subjects) to increase stability. Cross-validation results suggested the models were not over-fitted. This study indicates that EEG can be used to predict gross-performance degradations 5–15 min in advance

In-home evaluation of efficacy and titration of a mandibular advancement device for obstructive sleep apnea

There is increasing evidence that mandibular advancement devices (MADs) can be an effective treatment for some patients with obstructive sleep apnea, a highly prevalent chronic disease. In this study, the objectives were to objectively assess the effectiveness of MAD therapy using a limited channel recorder, and to develop a model for identifying patients who may be appropriate for MAD therapy as the initial treatment option. Thirty patients were prospectively recruited and studied at two independent dentist offices and the participants’ homes. Subjects wore the ARES Unicorder for two nights before insertion of the MAD, and again when the dentist determined that the patient had reached the titration endpoint. Self-reported measures of depression, sleepiness, and quality of life were obtained pre- and posttreatment. The reviewer was blinded to the study status while the physiological signals were being visually inspected. Significant reductions in the apnea/hypopnea index (AHI), hypoxemia measures, and snoring level were observed posttreatment. Twenty-seven of the 30 (90%) patients had a posttreatment AHI (using a 4% desaturation for hypopneas) below a clinical cut-off of 10. All but one patient (97%) exhibited at least a 50% decrease in AHI or had a posttreatment AHI ≤ 10. Significant differences in body mass index, weight, and neck circumference in patients with posttreatment AHIs above and below a clinical cut-off of five were identified. The linear regression used to predict the posttreatment AHI using pretreatment data resulted in an R2 of 0.68. The model correctly predicted two patients who were unable to obtain a posttreatment AHI of 10 or less. This study was designed to demonstrate two models of collaboration between a dental sleep medicine specialist and a sleep medicine physician in the monitoring of a patient treated with a MAD. The outcome data suggest that the limited channel recording system can be used as an alternative to laboratory polysomnography to reduce the cost of MAD treatment, and to improve the quality and consistency of posttreatment patient care

Evaluation Of An Eeg-Workload Model In The Aegis Simulation Environment

The integration of real-time electroencephalogram (EEG) workload indices into the man-machine interface could greatly enhance performance of complex tasks, transforming traditionally passive human-system interaction (HSI) into an active exchange where physiological indicators adjust the interaction to suit a user\u27s engagement level. The envisioned outcome is a closed-loop system that utilizes EEG and other physiological indices for dynamic regulation and optimization of HSI in real-time. As a first step towards a closed-loop system, five individuals performed as identification supervisors (IDSs) in an Aegis command and control (C2) simulated environment, a combat system with advanced, automatic detect-and-track, multi-function phased array radar. The Aegis task involved monitoring multiple data sources (i.e., missile-tracks, alerts, queries, resources), detecting required actions, responding appropriately, and ensuring system status remains within desired parameters. During task operation, a preliminary workload measure calculated in real-time for each second of EEG and was used to manipulate the Aegis task demands. In post-hoc analysis, the use of a five-level workload measure to detect cognitively challenging events was evaluated. Events in decreasing order of difficulty were track selection-identification, alert-responses, booking-tracks, and queries. High/extreme EEG-workload occurred during high cognitive-load tasks with a detection efficiency approaching 100% for selection-identification and alert-responses, 77% for hooking-tracks and 70% for queries. Over 95% of high/extreme EEG-workload across participants occurred during high-difficulty events (false positive rate \u3c 5%). The high/extreme workload occurred between 25-30% of time. These results suggest an intelligent closed-loop system incorporating EEG-workload measures could be designed to re-allocate tasks and aid in efficiently streamlining a user\u27s cognitive workload. Such an approach could ensure the operator remains uninterrupted during high/extreme workload periods, thereby resulting in increased productivity and reduced errors

A systematic comparison of factors that could impact treatment recommendations for patients with Positional Obstructive Sleep Apnea (POSA)

Systematically compare four criteria for Positional Obstructive Sleep Apnea (POSA) based on AASM 2007 and 2012 hypopnea scoring definitions

Retrospective cross-validation of automated sleep staging using electroocular recording in patients with and without sleep disordered breathing

Abstract Background Alterations of sleep duration and architecture have been associated with increased morbidity and mortality, and specifically linked to chronic cardiovascular disease and psychiatric disorders, such as type 2 diabetes or depression. Measurement of sleep quality to assist in the diagnosis or treatment of these diseases is not routinely performed due to the complexity and cost of conventional methods. The objective of this study is to cross-validate the accuracy of an automated algorithm that stages sleep from the EEG signal acquired with sensors that can be self-applied by patients. Methods This retrospective study design included polymsomnographic records from 19 presumably healthy individuals and 68 patients suspected of having sleep disordered breathing (SDB). Epoch-by-epoch comparisons were made between manual vs. automated sleeps staging (from the left and right electrooculogram) with the impact of SDB severity considered. Results Both scoring methods reported decreased Stage N3 and REM and increased wake and N1 as SDB severity increased. Inter-class correlations and Kappa coefficients were strong across all stages except N1. Agreements across all epochs for subjects with normal and patients with mild SDB were: wake = 80%, N1 = 25%, N2 = 78%, N3 = 84% and REM = 75%. Agreement decreased in patients with moderate and severe SDB amounting to: wake = 71%, N1 = 30%, N2 = 71%, N3 = 65%, and REM = 67%. Differences in detection of sleep onset were within three-minutes in 48 % of the subjects and 10-min in 73 % of the cases and were not impacted by SDB severity. Automated staging slightly underestimated total sleep time but this difference had a limited impact on the respiratory disturbance indexes. Conclusions This cross-validation study demonstrated that measurement of sleep architecture obtained from a single-channel of forehead EEG can be equivalent to between-rater agreement using conventional manual scoring. The accuracies obtained with automated sleep staging were inversely proportional to SDB severity at a rate similar to manual scorers. These results suggest that the automated sleep staging used in this study may prove useful in evaluating sleep quality in patients with chronic diseases.</p

Patients with positional versus nonpositional obstructive sleep apnea: A retrospective study of risk factors associated with apnea-hypopnea severity

Objective The aim of this study was to investigate the differences in and risk factors for positional and nonpositional obstructive sleep apnea (OSA). Study design One hundred twenty-three nonpositional (supine apnea-hypopnea index [AHI] < 2 times the lateral AHI), 218 positional (supine AHI < 2 times the lateral AHI), and 109 age-, gender-, and BMI-matched patients with positional OSA performed 2 nights of sleep study. Gender, age, BMI, and percentage of time in supine position, and percentage of time snoring louder than 40 dB were evaluated as risk factors. Results Both unmatched positional and matched positional patients had less severe overall AHI values, higher mean SpO 2, lower percentage time SpO 2 less than 90%, and lower percentage of time snoring when compared with the nonpositional group. Overall AHI scores were associated with increasing age and percentage of time snoring for positional and nonpositional groups. However, BMIs were associated with the overall AHI only in the nonpositional group. Conclusion The influence of position on OSA severity may contribute to the choice and prognosis of treatment and may represent 2 distinct groups with probable anatomic differences. ⓒ 2010 Mosby, Inc.