Automatic diagnosis of sleep apnea from biomedical signals using artificial intelligence techniques: Methods, challenges, and future works

P Moridian, A Shoeibi, M Khodatars… - … : Data Mining and …, 2022 - Wiley Online Library
Apnea is a sleep disorder that stops or reduces airflow for a short time during sleep. Sleep
apnea may last for a few seconds and happen for many while sleeping. This reduction in …

Current status and prospects of automatic sleep stages scoring

M Gaiduk, Á Serrano Alarcón, R Seepold… - Biomedical engineering …, 2023 - Springer
The scoring of sleep stages is one of the essential tasks in sleep analysis. Since a manual
procedure requires considerable human and financial resources, and incorporates some …

A deep learning method approach for sleep stage classification with EEG spectrogram

C Li, Y Qi, X Ding, J Zhao, T Sang, M Lee - International Journal of …, 2022 - mdpi.com
The classification of sleep stages is an important process. However, this process is time-
consuming, subjective, and error-prone. Many automated classification methods use …

SleepContextNet: A temporal context network for automatic sleep staging based single-channel EEG

C Zhao, J Li, Y Guo - Computer Methods and Programs in Biomedicine, 2022 - Elsevier
Background and objective: Single-channel EEG is the most popular choice of sensing
modality in sleep staging studies, because it widely conforms to the sleep staging …

SleepFCN: A fully convolutional deep learning framework for sleep stage classification using single-channel electroencephalograms

N Goshtasbi, R Boostani, S Sanei - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Sleep is a vital process of our daily life as we roughly spend one-third of our lives asleep. In
order to evaluate sleep quality and potential sleep disorders, sleep stage classification is a …

L-SeqSleepNet: Whole-cycle long sequence modelling for automatic sleep staging

H Phan, KP Lorenzen, E Heremans… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal
dependency in the sleep data. Yet, exploring this long-term dependency when developing …

Series arc fault detection based on wavelet compression reconstruction data enhancement and deep residual network

S Zhang, N Qu, T Zheng, C Hu - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Series arc fault is the main cause of electrical fire. Because of the complex load types and
the randomness of arc fault in low-voltage distribution system, it is difficult to obtain fault data …

Deep learning model with adaptive regularization for EEG-based emotion recognition using temporal and frequency features

A Samavat, E Khalili, B Ayati, M Ayati - IEEE Access, 2022 - ieeexplore.ieee.org
Since EEG signal acquisition is non-invasive and portable, it is convenient to be used for
different applications. Recognizing emotions based on Brain-Computer Interface (BCI) is an …

A lightweight segmented attention network for sleep staging by fusing local characteristics and adjacent information

W Zhou, H Zhu, N Shen, H Chen, C Fu… - … on Neural Systems …, 2022 - ieeexplore.ieee.org
Sleep staging is the essential step in sleep quality assessment and sleep disorders
diagnosis. However, most current automatic sleep staging approaches use recurrent neural …

Single-channel EEG sleep staging based on data augmentation and cross-subject discrepancy alleviation

Z He, L Du, P Wang, P Xia, Z Liu, Y Song… - Computers in biology …, 2022 - Elsevier
Automatic sleep stage classification is an effective technology compared to conventional
artificial visual inspection in the field of sleep staging. Numerous algorithms based on …