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 …

A deep transfer learning approach for wearable sleep stage classification with photoplethysmography

M Radha, P Fonseca, A Moreau, M Ross, A Cerny… - NPJ digital …, 2021 - nature.com
Unobtrusive home sleep monitoring using wrist-worn wearable photoplethysmography
(PPG) could open the way for better sleep disorder screening and health monitoring …

Objective sleep assessments for healthy people in environmental research: A literature review

X Xu, Z Lian - Indoor air, 2022 - Wiley Online Library
To date, although many studies had focused on the impact of environmental factors on
sleep, how to choose the proper assessment method for objective sleep quality was often …

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 …

Automatic sleep stage classification using deep learning: signals, data representation, and neural networks

P Liu, W Qian, H Zhang, Y Zhu, Q Hong, Q Li… - Artificial Intelligence …, 2024 - Springer
In clinical practice, sleep stage classification (SSC) is a crucial step for physicians in sleep
assessment and sleep disorder diagnosis. However, traditional sleep stage classification …

Mixed-input deep learning approach to sleep/wake state classification by using EEG signals

MN Hasan, I Koo - Diagnostics, 2023 - mdpi.com
Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health
issues from human sleep data. Manual sleep staging requires human expertise, which is …

Fusing YOLOv5s-MediaPipe-HRV to classify engagement in E-learning: From the perspective of external observations and internal factors

J Wang, S Yuan, T Lu, H Zhao, Y Zhao - Knowledge-Based Systems, 2024 - Elsevier
The rapid advancements in computer vision technology present significant potential for the
automatic recognition of learner engagement in E-learning. We conducted a two-stage …

Classifier for the functional state of the respiratory system via descriptors determined by using multimodal technology

SA Filist, RT Al-Kasasbeh, OV Shatalova… - Computer methods in …, 2023 - Taylor & Francis
Currently, intelligent systems built on a multimodal basis are used to study the functional
state of living objects. Its essence lies in the fact that a decision is made through several …

[HTML][HTML] On the feature extraction process in machine learning. An experimental study about guided versus non-guided process in falling detection systems

E Escobar-Linero, F Luna-Perejón… - … Applications of Artificial …, 2022 - Elsevier
Falls are current events that can lead to severe injuries and even accidental deaths among
the population, especially the elderly. Since them usually live alone and their contact with …

Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals

R Casal, LE Di Persia, G Schlotthauer - Journal of Computational Science, 2022 - Elsevier
Sleep disorders are very widespread in the world population and suffer from a generalized
underdiagnosis, given the complexity of their diagnostic methods. Therefore, there is an …