Sleep and Vigilance最新文献

Purpose: Persistent sustained attention deficit (SAD) after continuous positive airway pressure (CPAP) treatment is a source of quality of life and occupational impairment in obstructive sleep apnea (OSA). However, persistent SAD is difficult to predict in patients initiated on CPAP treatment. We performed secondary analyses of brain magnetic resonance (MR) images in treated OSA participants, using deep learning, to predict SAD.

Methods: 26 middle-aged men with CPAP use of more than 6 hours daily and MR imaging were included. SAD was defined by psychomotor vigilance task lapses of more than 2. 17 participants had SAD and 9 were without SAD. A Convolutional Neural Network (CNN) model was used for classifying the MR images into +SAD and -SAD categories.

Results: The CNN model achieved an accuracy of 97.02±0.80% in classifying MR images into +SAD and -SAD categories. Assuming a threshold of 90% probability for the MR image being correctly classified, the model provided a participant-level accuracy of 99.11±0.55% and a stable image level accuracy of 97.45±0.63%.

Conclusion: Deep learning methods, such as the proposed CNN model, can accurately predict persistent SAD based on MR images. Further replication of these findings will allow early initiation of adjunctive pharmacologic treatment in high-risk patients, along with CPAP, to improve quality of life and occupational fitness. Future augmentation of this approach with explainable artificial intelligence methods may elucidate the neuroanatomical areas underlying persistent SAD to provide mechanistic insights and novel therapeutic targets.

Purpose: The study aimed to (a) assess the sleep pattern changes and the level of fatigue among COVID positive adults (b) determine the association of sociodemographic and lifestyle factors (age, gender, marital status, occupation, income, exercise, nap, diet, and comorbidities) on sleep pattern and level of fatigue c) examine the relationship between sleep and fatigue, and between sleep problems, sleep quality and fatigue, among a community sample of COVID-19 affected adults.

Methods: A non-experimental, descriptive, cross-sectional survey design was used. Participants were adults, between 18 and 63 years (n = 782), who tested positive for COVID-19 infection using RT-PCR or Antigen test, confined to home quarantine/under observation, and without any complications. Data was collected using the socio-demographic-sleep and related activity questionnaire, Fatigue Assessment Scale, and Sleep Quality Scale.

Results: A majority of the participants reported either mild to moderate sleep quality problems (97.31%) and 377 of them (48.21%) reported fatigue levels. A significant association between sleep quality and fatigue with gender, and lifestyle factors such as sleep duration, food intake, napping, exercise pattern, and influence of COVID-19 on livelihood after being affected with COVID-19, and time of experiencing sleep problems after COVID-19 infection (all, p ˂ 0.01) were observed, as well as age with sleep quality. Poor sleep quality and fatigue were significantly correlated with each other, and also with sleep problems before being affected with COVID-19 (p = 0.000).

Conclusions: The study has shown that COVID-19 has an effect on an individual's demographic factors and a multitude of lifestyle factors, and highlights the need for post-COVID-19 monitoring even after recovery from the disease.