Comparative analysis of different characteristics of automatic sleep stages
D Zhao, Y Wang, Q Wang, X Wang - Computer methods and programs in …, 2019 - Elsevier
Background and objective With the acceleration of social rhythm and the increase of
pressure, there are various sleep problems among people. Sleep staging is an important …
pressure, there are various sleep problems among people. Sleep staging is an important …
Comprehensive Review of Feature Extraction Techniques for sEMG Signal Classification: From Handcrafted Features to Deep Learning Approaches
Surface Electromyography (sEMG) has become an essential tool in various fields, including
prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent …
prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent …
Deep learning for processing electromyographic signals: A taxonomy-based survey
Deep Learning (DL) has been recently employed to build smart systems that perform
incredibly well in a wide range of tasks, such as image recognition, machine translation, and …
incredibly well in a wide range of tasks, such as image recognition, machine translation, and …
A novel automated robust dual-channel EEG-based sleep scoring system using optimal half-band pair linear-phase biorthogonal wavelet filter bank
Nowadays, the hectic work life of people has led to sleep deprivation. This may further result
in sleep-related disorders and adverse physiological conditions. Therefore, sleep study has …
in sleep-related disorders and adverse physiological conditions. Therefore, sleep study has …
End-to-end sleep staging with raw single channel EEG using deep residual convnets
Humans approximately spend a third of their life slee**, which makes monitoring sleep an
integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for …
integral part of well-being. In this paper, a 34-layer deep residual ConvNet architecture for …
Sleep EEG analysis utilizing inter-channel covariance matrices
Background Sleep is vital for normal body functions as sleep disorders can adversely affect
a person. Electroencephalographic (EEG) signals indicate brain functions and have …
a person. Electroencephalographic (EEG) signals indicate brain functions and have …
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data
As the field of deep learning has grown in recent years, its application to the domain of raw
resting-state electroencephalography (EEG) has also increased. Relative to traditional …
resting-state electroencephalography (EEG) has also increased. Relative to traditional …
Multi-branch convolutional neural network for automatic sleep stage classification with embedded stage refinement and residual attention channel fusion
T Zhu, W Luo, F Yu - Sensors, 2020 - mdpi.com
Automatic sleep stage classification of multi-channel sleep signals can help clinicians
efficiently evaluate an individual's sleep quality and assist in diagnosing a possible sleep …
efficiently evaluate an individual's sleep quality and assist in diagnosing a possible sleep …
Performance evaluation of a smart bed technology against polysomnography
The Sleep Number smart bed uses embedded ballistocardiography, together with network
connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing …
connectivity, signal processing, and machine learning, to detect heart rate (HR), breathing …
[HTML][HTML] Machine and deep learning in molecular and genetic aspects of sleep research
Epidemiological sleep research strives to identify the interactions and causal mechanisms
by which sleep affects human health, and to design intervention strategies for improving …
by which sleep affects human health, and to design intervention strategies for improving …