The emergence of AI-based wearable sensors for digital health technology: a review
Disease diagnosis and monitoring using conventional healthcare services is typically
expensive and has limited accuracy. Wearable health technology based on flexible …
expensive and has limited accuracy. Wearable health technology based on flexible …
Automated detection of sleep stages using deep learning techniques: A systematic review of the last decade (2010–2020)
Sleep is vital for one's general well-being, but it is often neglected, which has led to an
increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep …
increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep …
A deep transfer learning framework for sleep stage classification with single-channel EEG signals
The polysomnogram (PSG) is the gold standard for evaluating sleep quality and disorders.
Attempts to automate this process have been hampered by the complexity of the PSG …
Attempts to automate this process have been hampered by the complexity of the PSG …
Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis
AM Tăuţan, AC Rossi, R de Francisco… - Biomedical Engineering …, 2021 - degruyter.com
Methods developed for automatic sleep stage detection make use of large amounts of data
in the form of polysomnographic (PSG) recordings to build predictive models. In this study …
in the form of polysomnographic (PSG) recordings to build predictive models. In this study …
The effect of light therapy on electroencephalographic sleep in sleep and circadian rhythm disorders: a scoping review
Light therapy is used to treat sleep and circadian rhythm disorders, yet there are limited
studies on whether light therapy impacts electroencephalographic (EEG) activity during …
studies on whether light therapy impacts electroencephalographic (EEG) activity during …
DeepSleep 2.0: automated sleep arousal segmentation via deep learning
R Fonod - AI, 2022 - mdpi.com
DeepSleep 2.0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully
convolutional deep neural network, which achieved the highest unofficial score in the 2018 …
convolutional deep neural network, which achieved the highest unofficial score in the 2018 …
Deep-Learning-Based Automated Anomaly Detection of EEGs in Intensive Care Units
JCH Wu, NC Liao, TH Yang, CC Hsieh, JA Huang… - Bioengineering, 2024 - mdpi.com
An intensive care unit (ICU) is a special ward in the hospital for patients who require
intensive care. It is equipped with many instruments monitoring patients' vital signs and …
intensive care. It is equipped with many instruments monitoring patients' vital signs and …
Classification of hemodynamics scenarios from a public radar dataset using a deep learning approach
Contact-free sensors offer important advantages compared to traditional wearables. Radio-
frequency sensors (eg, radars) offer the means to monitor cardiorespiratory activity of people …
frequency sensors (eg, radars) offer the means to monitor cardiorespiratory activity of people …
A convolutional network for the classification of sleep stages
I Fernández-Varela, E Hernández-Pereira… - Proceedings, 2018 - mdpi.com
The classification of sleep stages is a crucial task in the context of sleep medicine. It involves
the analysis of multiple signals thus being tedious and complex. Even for a trained physician …
the analysis of multiple signals thus being tedious and complex. Even for a trained physician …
A Systematic Review on Latest Approaches of Automated Sleep Staging System Using Machine Intelligence Techniques
Abstract Background and Objective: Sleep staging plays a vital role in sleep research
because sometimes sleep recording errors may cause severe problems like …
because sometimes sleep recording errors may cause severe problems like …