Towards an Accessible Speech-based Obstructive Sleep Apnea Screening Tool for Underserved Populations.

Obstructive sleep apnea (OSA) is a chronic respiratory disorder characterized by recurrent interruptions in breathing during sleep. OSA is highly prevalent, affecting 30-70% of people with chronic conditions like hypertension and substance use. The gold standard for clinical OSA diagnosis is the polysomnography (PSG) test, which is a rather cumbersome and expensive procedure, and accordingly can be quite inconvenient for patients.

Snoring Vibration: A Robust Measure for Predicting Treatment Outcome for Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) represents a prevalent condition impacting over 9% of the general adult population. Various treatment options have been clinically proposed and utilized, with a particular focus on continuous positive airway pressure (CPAP) and oral appliances due to their overall effectiveness and higher adherence rates. CPAP therapy has demonstrated greater effectiveness but lower adherence compared to oral appliances.

Estimating the severity of obstructive sleep apnea during wakefulness using speech: A review.

Obstructive sleep apnea (OSA) is a chronic breathing disorder during sleep that affects 10-30% of adults in North America. The gold standard for diagnosing OSA is polysomnography (PSG). However, PSG has several drawbacks, for example, it is a cumbersome and expensive procedure, which can be quite inconvenient for patients.

Textile-based Wearable to Monitor Heart Activity in Paediatric Population: A Pilot Study.

Background: Cardiac monitoring for children with heart disease still employs common clinical techniques that require visits to hospital either in an ambulatory or inpatient setting. Frequent cardiac monitoring, such as heart rate monitoring, can limit children's physical activity and quality of life. The main objective of this study is to evaluate the performance of a textile-based device (SKIIN) in measuring heart rate (HR) in different tasks: lying down, sitting, standing, exercising, and cooling down.

ETucker: a constrained tensor decomposition for single trial ERP extraction.

In this paper, we propose a new tensor decomposition to extract event-related potentials (ERP) by adding a physiologically meaningful constraint to the Tucker decomposition.We analyze the performance of the proposed model and compare it with Tucker decomposition by synthesizing a dataset. The simulated dataset is generated using a 12th-order autoregressive model in combination with independent component analysis (ICA) on real no-task electroencephalogram (EEG) recordings.