Objective: Circadian and sleep dysfunction have long been symptomatic hallmarks of a variety of devastating neurodegenerative conditions. The gold standard for sleep monitoring is overnight sleep in a polysomnography (PSG) laboratory. However, this method has several limitations such as availability, cost and being labour-intensive. In recent years there has been a heightened interest in home-based sleep monitoring via wearable sensors. Our objective was to demonstrate the use of printed electrode technology as a novel platform for sleep monitoring.
Approach: Printed electrode arrays offer exciting opportunities in the realm of wearable electrophysiology. In particular, soft electrodes can conform neatly to the wearer's skin, allowing user convenience and stable recordings. As such, soft skin-adhesive non-gel-based electrodes offer a unique opportunity to combine electroencephalography (EEG), electromyography (EMG), electrooculography (EOG) and facial EMG capabilities to capture neural and motor functions in comfortable non-laboratory settings. In this investigation temporary-tattoo dry electrode system for sleep staging analysis was designed, implemented and tested.
Main Results: EMG, EOG and EEG were successfully recorded using a wireless system. Stable recordings were achieved both at a hospital environment and a home setting. Sleep monitoring during a 6 h session shows clear differentiation of sleep stages.
Significance: The new system has great potential in monitoring sleep disorders in the home environment. Specifically, it may allow the identification of disorders associated with neurological disorders such as rapid eye movement (REM) sleep behavior disorder.
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http://dx.doi.org/10.1088/1741-2552/aafa05 | DOI Listing |
Sleep Breath
January 2025
Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China.
Background And Objective: There is no satisfactory treatment for obstructive sleep apnea (OSA) in patients with interstitial lung disease (ILD) because of poor tolerance of positive airway pressure (PAP) therapy. Supplemental oxygen therapy has been shown to reduce hypoxemia and is well tolerated in patients with ILD. However, little is known about the effect of nocturnal oxygen supplementation (NOS) on OSA in patients with ILD.
View Article and Find Full Text PDFStudy Objectives: Poor sleep may play a role in the risk of dementia. However, few studies have investigated the association between polysomnography (PSG)-derived sleep architecture and dementia incidence. We examined the relationship between sleep macro-architecture and dementia incidence across five US-based cohort studies from the Sleep and Dementia Consortium (SDC).
View Article and Find Full Text PDFMayo Clin Proc Digit Health
December 2024
Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
View Article and Find Full Text PDFJ Metab Bariatr Surg
December 2024
Department of Surgery, Kosin University, College of Medicine, Busan, Korea.
Purpose: Obesity is a major risk factor for obstructive sleep apnea (OSA), associated with conditions like type 2 diabetes, hypertension, stroke, cancer, and premature death. OSA involves sleep-breathing interruptions, with over 60% of obese individuals diagnosed through polysomnography. This study explores sleep issues in individuals considering bariatric surgery.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, No.727 Jingming South Road, Kunming, 650504 Yunnan China.
For diagnosing mental health conditions and assessing sleep quality, the classification of sleep stages is essential. Although deep learning-based methods are effective in this field, they often fail to capture sufficient features or adequately synthesize information from various sources. For the purpose of improving the accuracy of sleep stage classification, our methodology includes extracting a diverse array of features from polysomnography signals, along with their transformed graph and time-frequency representations.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!