AIMC Topic: Sleep

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Detecting arousals and sleep from respiratory inductance plethysmography.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Accurately identifying sleep states (REM, NREM, and Wake) and brief awakenings (arousals) is essential for diagnosing sleep disorders. Polysomnography (PSG) is the gold standard for such assessments but is costly and requires overnight monit...

Neural models for detection and classification of brain states and transitions.

Communications biology
Exploring natural or pharmacologically induced brain dynamics, such as sleep, wakefulness, or anesthesia, provides rich functional models for studying brain states. These models allow detailed examination of unique spatiotemporal neural activity patt...

CHMMConvScaleNet: a hybrid convolutional neural network and continuous hidden Markov model with multi-scale features for sleep posture detection.

Scientific reports
Sleep posture, a vital aspect of sleep wellness, has become a crucial focus in sleep medicine. Studies show that supine posture can lead to a higher occurrence of obstructive sleep apnea, while lateral posture might reduce it. For bedridden patients,...

Personalized Health Prediction AI Models Using Transfer Learning and Strategic Overfitting on Wearable Device Data.

Journal of medical systems
The increasing availability of wearable device data provides an opportunity for developing personalized models for health monitoring and condition prediction. Unlike conventional approaches that rely on pooled data from diverse individuals, our study...

The future of paediatric sleep medicine: a blueprint for advancing the field.

Journal of sleep research
Paediatric sleep medicine has rapidly evolved and expanded over the past half century as it became increasingly recognised as a unique field related to but distinct from adult sleep medicine. In looking forward to the next years, the focus of the fol...

Continuous real-time detection and management of comprehensive mental states using wireless soft multifunctional bioelectronics.

Biosensors & bioelectronics
Quantitatively measuring human mental states that profoundly affect cognition, behavior, and recovery would revolutionize personalized digital healthcare. Detecting fatigue, stress, and sleep is particularly important due to their interdependence: pe...

FlyVISTA, an integrated machine learning platform for deep phenotyping of sleep in .

Science advances
There is great interest in using genetically tractable organisms such as to gain insights into the regulation and function of sleep. However, sleep phenotyping in has largely relied on simple measures of locomotor inactivity. Here, we present FlyVI...

Actigraphy against 32-hour polysomnography in patients with suspected idiopathic hypersomnia.

Journal of sleep research
Actigraphy, a tool known for investigating sleep-wake patterns at home, lacks scientific validation in hypersomnolent subjects. We aim to validate an actigraphy-based sleep-wake prediction algorithm against 32-h continuous polysomnography in patients...

A deep learning-enabled smart garment for accurate and versatile monitoring of sleep conditions in daily life.

Proceedings of the National Academy of Sciences of the United States of America
In wearable smart systems, continuous monitoring and accurate classification of different sleep-related conditions are critical for enhancing sleep quality and preventing sleep-related chronic conditions. However, the requirements for device-skin cou...

Human sleep position classification using a lightweight model and acceleration data.

Sleep & breathing = Schlaf & Atmung
PURPOSE: This exploratory study introduces a portable, wearable device using a single accelerometer to monitor twelve sleep positions. Targeted for home use, the device aims to assist patients with mild conditions such as gastroesophageal reflux dise...