[HTML][HTML] Deep learning intervention for health care challenges: some biomedical domain considerations
The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care
problems has received unprecedented attention in the last decade. The technique has …
problems has received unprecedented attention in the last decade. The technique has …
Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective
A Bandyopadhyay, C Goldstein - Sleep and Breathing, 2023 - Springer
Background The past few years have seen a rapid emergence of artificial intelligence (AI)-
enabled technology in the field of sleep medicine. AI refers to the capability of computer …
enabled technology in the field of sleep medicine. AI refers to the capability of computer …
Multi-view spatial-temporal graph convolutional networks with domain generalization for sleep stage classification
Sleep stage classification is essential for sleep assessment and disease diagnosis.
Although previous attempts to classify sleep stages have achieved high classification …
Although previous attempts to classify sleep stages have achieved high classification …
Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats
Arrhythmia is a cardiac conduction disorder characterized by irregular heartbeats.
Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) …
Abnormalities in the conduction system can manifest in the electrocardiographic (ECG) …
Joint classification and prediction CNN framework for automatic sleep stage classification
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders.
This paper proposes a joint classification-and-prediction framework based on convolutional …
This paper proposes a joint classification-and-prediction framework based on convolutional …
Identifying mental fatigue of construction workers using EEG and deep learning
Y Wang, Y Huang, B Gu, S Cao, D Fang - Automation in Construction, 2023 - Elsevier
Construction workers frequently experience mental fatigue owing to the high cognitive load
of their tasks in a dynamic, complex environment, diminishing their cognitive ability and …
of their tasks in a dynamic, complex environment, diminishing their cognitive ability and …
[PDF][PDF] GraphSleepNet: Adaptive spatial-temporal graph convolutional networks for sleep stage classification.
Sleep stage classification is essential for sleep assessment and disease diagnosis.
However, how to effectively utilize brain spatial features and transition information among …
However, how to effectively utilize brain spatial features and transition information among …
A survey of complex-valued neural networks
Artificial neural networks (ANNs) based machine learning models and especially deep
learning models have been widely applied in computer vision, signal processing, wireless …
learning models have been widely applied in computer vision, signal processing, wireless …
Learning spatial–spectral–temporal EEG features with recurrent 3D convolutional neural networks for cross-task mental workload assessment
Mental workload assessment is essential for maintaining human health and preventing
accidents. Most research on this issue is limited to a single task. However, cross-task …
accidents. Most research on this issue is limited to a single task. However, cross-task …
Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement
D Jiang, Y Lu, MA Yu, W Yuanyuan - Expert Systems with Applications, 2019 - Elsevier
Sleep stage classification is a most important process in sleep scoring which is used to
evaluate sleep quality and diagnose sleep-related diseases. Compared to complex sleep …
evaluate sleep quality and diagnose sleep-related diseases. Compared to complex sleep …