Sleep Disorders

Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks.

Novel approaches are needed to accurately classify and monitor sleep patterns in older adults, particularly those with cognitive impairment and non-normative sleep. Traditional methods ignore underlying sleep architecture in these patient populations, and other modern approaches tend to focus on healthy, normative patient populations. In this paper, we developed a model using a long-short-term mem...

Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry.

Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagnosis (overnight polysomnography) has several limitations, which has led to the search for alternative tests. In this sense, automated analysis of overn...

[A Domestic Diagnosis System for Early Restless Legs Syndrome Based on Deep Learning].

Restless legs syndrome,as a common sleep disorder,has nowadays long been diagnosed by self-rating scale and polysomnography.In this paper,a domestic d...

Expert-level sleep scoring with deep neural networks.

OBJECTIVES: Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disor...

The effect of continuous positive airway pressure on pulmonary function may depend on the basal level of forced expiratory volume in 1 second.

BACKGROUND: The coexistence of chronic obstructive pulmonary disease (COPD) and obstructive sleep apnea (OSA), also referred to as overlap syndrome (O...

A Two Stage Approach for the Automatic Detection of Insomnia.

Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exis...

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep exp...

[Effect of Acupuncture at Points in Heel Vessel for Circadian Genes of 1 and 2 mRNAs in the Suprachiasmatic Nucleus in Insomnia Rats].

OBJECTIVE: To investigate the influence and mechanism of acupuncture at the points in Heel Vessel for the circadian clock genes of Period () 1 and 2 ...

Assessment of support vector machines and convolutional neural networks to detect snoring using Emfit mattress.

Snoring (SN) is an essential feature of sleep breathing disorders, such as obstructive sleep apnea (OSA). In this study, we evaluate epoch-based snori...

Optimization of Melatonin Dissolution from Extended Release Matrices Using Artificial Neural Networking.

BACKGROUND: Efficacy of melatonin in treating sleep disorders has been demonstrated in numerous studies. Being with short half-life, melatonin needs t...

Towards a Wireless Smart Polysomnograph Using Symbolic Fusion.

Polysomnography is the gold standard test for sleep disorders among which the Sleep Apnea Syndrome (SAS) is considered a public health issue because o...

Effect of Transtympanic Injection of Melatonin on Cisplatin-Induced Ototoxicity.

OBJECTIVE: Cisplatin is a chemotherapeutic agent that is widely used in cancer treatment. Numerous side effects have been detected, one of which is ot...

[The curative effect analysis of continuous positive airway pressure combined with modified oral appliance in the treatment of severe OSAHS].

OBJECTIVE: To evaluate the curative effect of continuous positive airway pressure(CPAP) combined with modified oral appliance (MOA) in the treatment o...

[Validation of the advanced event detection in patients with sleep apnea hypopnea syndrome using auto-CPAP treatment].

OBJECTIVE: To validate the use of the event detection capabilities in an auto-CPAP system used by patients with sleep apnea hypopnea syndrome (SAHS).

Sleep snoring detection using multi-layer neural networks.

Snoring detection is important for diagnosing obstructive sleep apnea syndrome (OSAS) and other respiratory sleep disorders. In general, audio signal ...