Wireless wearable sensors can facilitate rapid detection of sleep apnea in hospitalized stroke patients.

STUDY OBJECTIVES: To evaluate wearable devices and machine learning for detecting sleep apnea in patients with stroke at an acute inpatient rehabilitation facility (IRF). METHODS: A total of 76 individuals with stroke wore a standard home sleep apnea test (ApneaLink Air), a multimodal, wireless wearable sensor system (ANNE), and a research-grade actigraphy device (ActiWatch) for at least one night during their first week after IRF admission as part of a larger clinical trial. Logistic regression algorithms were trained to detect sleep apnea using biometric features obtained from the ANNE sensors and ground truth apnea rating from the ApneaLink Air. Multiple algorithms were evaluated using different sensor combinations and different apnea detection criteria based on the Apnea-Hypopnea Index (AHI≥5, AHI≥15). RESULTS: Seventy-one (96%) participants wore the ANNE sensors for multiple nights. In contrast, only forty-eight participants (63%) could be successfully assessed for OSA by ApneaLink; 28 (37%) refused testing. The best-performing model utilized photoplethysmography (PPG) and finger temperature features to detect moderate-severe sleep apnea (AHI≥15), with 88% sensitivity and a positive likelihood ratio (LR+) of 44.00. This model was tested on additional nights of ANNE data achieving 71% sensitivity (10.14 LR+) when considering each night independently and 86% accuracy when averaging multi-night predictions. CONCLUSIONS: This research demonstrates the feasibility of accurately detecting moderate-severe sleep apnea early in the stroke recovery process using wearable sensors and machine learning techniques. These findings can inform future efforts to improve early detection for post-stroke sleep disorders, thereby enhancing patient recovery and long-term outcomes.

Dual-task performance is associated with brain MRI Morphometry in individuals with mild cognitive impairment.

BACKGROUND AND PURPOSE: Cognitive impairment is a critical health problem in the elderly population. Research has shown that patients with mild cognitive impairment (MCI) may develop dementia in later years. Therefore, early identification of MCI could allow for interventions to help delay the progression of this devastating disease. Our objective in this study was to detect the early presence of MCI in elderly patients via neuroimaging and dual-task performance. METHODS: Brain MRI scans from 21 older adult volunteers, including cognitively healthy adults (HA, n = 9, age = 68-79 years) and mild cognitively impaired (MCI, n = 12, age = 66-92 years) were analyzed using automatic segmentation techniques. Regional volume, surface area, and thickness measures were correlated with simultaneous performance of motor and cognitive tasks (dual-task) within a novel upper-extremity function (UEF) test, using multivariate analysis of variance models. RESULTS: We found significant associations of dual-task performance with volume of five cortical brain regions (P ≤ .048) and thickness of 13 regions (P ≤ .043) within the frontal, temporal, and parietal lobes. There was a significant interaction effect of cognitive group on dual-task score for the inferior temporal gyrus volume (P ≤ .034), and the inferior parietal lobule, inferior temporal gyrus, and middle temporal gyrus average thickness (P ≤ .037). CONCLUSIONS: This study highlighted the potential of dual-tasking and MRI morphometric changes as a simple and accurate tool for early detection of cognitive impairment among community-dwelling older adults. The strong interaction effects of cognitive group on UEF dual-task score suggest higher association between atrophy of these brain structures and compromised dual-task performance among the MCI group.