ObjectiveAnalyze the impact of sleep disturbance on neurocognitive function in children recovering from mild traumatic brain injury (mTBI).BackgroundSleep disturbance of any nature is reported in more than half of all mTBI patients. The pathophysiology of sleep disturbance following mTBI is associated with structural and functional disruptions of sleep circuitry and circadian rhythm. Specifically in the pediatric population, untreated sleep disturbance has been shown to delay mTBI recovery and compound other morbidities including neurocognitive dysfunction.Design/MethodsA retrospective chart review of 118 pediatric patients (mean age = 14.56 ± 2.03 years) recovering from mTBI was performed. Epworth Sleepiness Scale (SF-8) results were analyzed in relation to CNS Vital Signs (CNSVS) neurocognitive test outcomes. SF-8 is a subjective estimation of a patient's daytime sleepiness. CNSVS uses a multitude of domains to objectively evaluate the overall neurocognitive status of a patient. Pearson correlations were calculated using a type I error of p < 0.05 between variables.ResultsEpworth Sleepiness Scale (SF-8) results showed 28.82% of participants experienced excessive daytime sleepiness sufficient enough to recommend medical attention. Upon further analysis, there was a significant negative correlation between SF-8 and CNSVS neurocognitive test outcomes including complex attention (r = −0.37; p = 0.0004), cognitive flexibility (r = −0.24; p = 0.0151), executive function (r = −0.21; p = 0.0350), and simple attention (r = −0.36; p = 0.0003) scores. This means as SF-8 scores increased (participants defined as excessively sleepy), neurocognitive function scores in these domains decreased. There was not enough evidence to conclude a significant correlation between other CNSVS domains and SF-8 (all p > 0.05).ConclusionsOur findings support the concern of neurocognitive dysfunction among pediatric mTBI patients with sleep disturbance. Further analysis is needed to determine if mTBI is the primary source or an exacerbating factor of sleep disturbance within this population. Nonetheless, these findings suggest a need for thorough evaluation when treating sleep concerns, irrespective of a history of childhood mTBI.
Epworth sleepiness scale is a self-assessment method in sleep medicine that has been proven to be a good predictor of obstructive sleep apnea. However, the over-reliance of the method making the process not socially distancing friendly enough in response to a global covid-19 pandemic. A study states that the Epworth sleepiness scale is correlated with the brainwave signal that commercial-grade EEG can capture. This study tried to train a classifier powered by CNN and deep learning that could perform as well as the Epworth with the objectiveness of brainwave signal. We test the classifier using the 20 university student using the Epworth sleepiness test beforehand. Then, we put the participant in 10 minutes EEG session, downsampling the data for normalization purposes and trying to predict the outcome of the ESS in respect of their brainwave state. The AI predict the reaching 65% of accuracy and 81% of sensitivity with just under 100.000 dataset which is excellent considering small dataset although this still have plenty room for improvement.
Background: Cognitive impairment is common and disabling in Parkinson’s disease (PD). Cognitive testing can be time consuming in the clinical setting. One rapid test to detect cognitive impairment in non-PD populations is the Clock Drawing Test (CDT), which calls upon the brain’s executive and visuospatial abilities to draw a clock designating a certain time. Objective: Test the hypothesis that PD participants would perform worse on CDT compared to controls and that CDT would correlate with other measures of cognition. Methods: This study evaluated two independent CDT scoring systems and differences in CDT performance between PD (N = 97) and control (N = 54) participants using a two-sample t-test. Pearson’s correlations were conducted between the CDT and tests of sleepiness (Epworth Sleepiness Scale) and vigilance (Psychomotor Vigilance Test); executive function (Trails B-A); and global cognition (Montreal Cognitive Assessment). Receiver operating characteristic curves were used to determine cut points on the CDT that identify individuals who need additional cognitive testing. Results: PD participants had worse performance on CDT compared to controls. The CDT was correlated with executive function (Trails B-A) and global cognition (Montreal Cognitive Assessment). The CDT correlated with vigilance (Psychomotor Vigilance Task) only in healthy controls. However, the CDT was not correlated with measures of sleepiness (Epworth Sleepiness Scale) in either group. A cut point of 9 on the Rouleau scale and 18 on the Mendez scale identified PD participants with cognitive impairment. Conclusion: The CDT is a rapid clinical cognitive assessment that is feasible in PD and correlates with other measures of cognition.
Background and aims: This study aimed to clarify the incidence and severity of habitual sleep disturbances in patients undergoing surgery. Methods: A survey was conducted to collect data in a cohort of patients awaiting abdominal surgery. The Insomnia Severity Index score was used as primary measurement, while the Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were used as secondary measurements. Findings: Overall, 119 patients were included in the analysis. The habitual sleep in the study cohort revealed high incidence of sleep difficulties and insomnia. Female sex and incidence of anxiety and depression were significantly related to all three scores, while age and employment were associated with Epworth Sleepiness Scale and Insomnia Severity Index scores, respectively. Conclusions: Female sex, experiencing depression or anxiety had severe negative impact on sleep in patients scheduled for elective or planned surgery. The prevalence of sleep problems in patients should be considered for optimising sleep to improve rehabilitation and reduce stress associated with surgery.
Detecting Excessive Daytime Sleepiness With CNN And Commercial Grade EEG
