Sleep Disorders

Coupling analysis of heart rate variability and cortical arousal using a deep learning algorithm.

Frequent cortical arousal is associated with cardiovascular dysfunction among people with sleep-disordered breathing. Changes in heart rate variability (HRV) can represent pathological conditions associated with autonomic nervous system dysfunction. Previous studies showed changes in cardiac activity due to cortical arousals. However, few studies have examined the instantaneous association between...

Sleep-Energy: An Energy Optimization Method to Sleep Stage Scoring.

Sleep is essential for physical and mental health. Polysomnography (PSG) procedures are labour-intensive and time-consuming, making diagnosing sleep disorders difficult. Automatic sleep staging using Machine Learning (ML) - based methods has been studied extensively, but frequently provides noisier predictions incompatible with typical manually annotated hypnograms. We propose an energy optimizati...

Modeling acute care utilization: practical implications for insomnia patients.

Machine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate...

Fast Sleep Stage Classification Using Cascaded Support Vector Machines with Single-Channel EEG Signals.

Long-term sleep stage monitoring is very important for the diagnosis and treatment of insomnia. With the development of wearable electroencephalogram ...

Pediatric Automatic Sleep Staging: A Comparative Study of State-of-the-Art Deep Learning Methods.

BACKGROUND: Despite the tremendous prog- ress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced al...

Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape.

Classifying sleep stages in real-time represents considerable potential, for instance in enabling interactive noise masking in noisy environments when...

A review of automated sleep disorder detection.

Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effecti...

A systematic review of deep learning methods for modeling electrocardiograms during sleep.

Sleep is one of the most important human physiological activities, and plays an essential role in human health. Polysomnography (PSG) is the gold stan...

Accurate contactless sleep apnea detection framework with signal processing and machine learning methods.

The detection of sleep apnea is critical for assessing sleep quality. It is also a proven biometric in diagnosing cardiovascular and other diseases. R...

Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm.

Polysomnography is manually scored by sleep experts. However, manual scoring is a time-consuming and labor-intensive task. The goal of this study was...

Robot-Assisted Laparoscopic Calyceo-Pyelostomy for Vascular Compression of the Upper Calyx (Fraley Syndrome).

Fraley's Syndrome is a rare anatomic vascular malformation described in 1966 where an aberrant crossing vessel compresses the upper infundibulum and l...

Insomnia disorder diagnosed by resting-state fMRI-based SVM classifier.

BACKGROUND: The main classification systems of sleep disorders are based on the subjective self-reported criteria. Objective measures are essential to...

Machine learning and geometric morphometrics to predict obstructive sleep apnea from 3D craniofacial scans.

BACKGROUND: Obstructive sleep apnea (OSA) remains massively underdiagnosed, due to limited access to polysomnography (PSG), the highly complex gold st...

Sleep staging classification based on a new parallel fusion method of multiple sources signals.

In the field of medical informatics, sleep staging is a challenging and time consuming task undertaken by sleep experts. The conventional method for s...

Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea.

BACKGROUND: Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current di...

Polysomnographic identification of anxiety and depression using deep learning.

Anxiety and depression are common psychiatric conditions associated with significant morbidity and healthcare costs. Sleep is an evolutionarily conser...

Impact of planting density and soaking seeds in melatonin solution on yield, secondary products content and antimicrobial activity of lovage plant.

Many studies worldwide have been done on the effect of medicinal uses of lovage plant but, very little works have been done on its production. In this...

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.

Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopne...

The effects of a sleep robot intervention on sleep, depression and anxiety in adults with insomnia - Study protocol of a randomized waitlist-controlled trial.

Insomnia is a common sleep disorder characterized by difficulties initiating sleep, maintaining sleep and/or early-morning awakenings. Hyperarousal is...

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.

Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive sympt...