Benchmark on a large cohort for sleep-wake classification with machine learning techniques
Accurately measuring sleep and its quality with polysomnography (PSG) is an expensive
task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However …
task. Actigraphy, an alternative, has been proven cheap and relatively accurate. However …
An attention based CNN-LSTM approach for sleep-wake detection with heterogeneous sensors
In this article, we propose an attention based convolutional neural network long short-term
memory (CNN-LSTM) approach for sleep-wake detection with heterogeneous sensor data …
memory (CNN-LSTM) approach for sleep-wake detection with heterogeneous sensor data …
Contactless body movement recognition during sleep via WiFi signals
Y Cao, F Wang, X Lu, N Lin, B Zhang… - IEEE Internet of …, 2019 - ieeexplore.ieee.org
Body movement is one of the most important indicators of sleep quality for elderly people
living alone. Body movement is crucial for sleep staging and can be combined with other …
living alone. Body movement is crucial for sleep staging and can be combined with other …
Objective ADHD diagnosis using convolutional neural networks over daily-life activity records
P Amado-Caballero… - IEEE journal of …, 2020 - ieeexplore.ieee.org
Attention Deficit/Hyperactivity Disorder (ADHD) is the most common neurobehavioral
disorder in children and adolescents. However, its etiology is still unknown, and this hinders …
disorder in children and adolescents. However, its etiology is still unknown, and this hinders …
Deep-ACTINet: End-to-end deep learning architecture for automatic sleep-wake detection using wrist actigraphy
Sleep scoring is the first step for diagnosing sleep disorders. A variety of chronic diseases
related to sleep disorders could be identified using sleep-state estimation. This paper …
related to sleep disorders could be identified using sleep-state estimation. This paper …
A novel ensemble deep learning approach for sleep-wake detection using heart rate variability and acceleration
Sleep-wake detection is of great importance for the measurement of sleep quality. In this
article, a novel ensemble deep learning framework is proposed to detect sleep-wake states …
article, a novel ensemble deep learning framework is proposed to detect sleep-wake states …
Sleep–wake stage detection with single channel ECG and hybrid machine learning model in patients with obstructive sleep apnea
Sleep staging is an important step in the diagnosis of obstructive sleep apnea (OSA) and
this step is performed by a physician who visually scores the electroencephalography …
this step is performed by a physician who visually scores the electroencephalography …
A baseline for detecting out-of-distribution examples in image captioning
Image captioning research achieved breakthroughs in recent years by developing neural
models that can generate diverse and high-quality descriptions for images drawn from the …
models that can generate diverse and high-quality descriptions for images drawn from the …
Method for Activity Sleep Harmonization (MASH): a novel method for harmonizing data from two wearable devices to estimate 24-h sleep–wake cycles
Background Daily 24-h sleep–wake cycles have important implications for health, however
researcher preferences in choice and location of wearable devices for behavior …
researcher preferences in choice and location of wearable devices for behavior …
Advancing sleep detection by modelling weak label sets: A novel weakly supervised learning approach
Understanding sleep and activity patterns plays a crucial role in physical and mental health.
This study introduces a novel approach for sleep detection using weakly supervised …
This study introduces a novel approach for sleep detection using weakly supervised …