Revolutionizing Healthcare Delivery Through Wireless Wearable Antenna Frameworks: Current Trends and Future Prospects
A Segun, A Telukdarie - IEEE access, 2023 - ieeexplore.ieee.org
The arrival of various mechanism applications to healthcare is gaining more attention with
various novel breakthroughs in digitalizing healthcare. The use of technology in improving …
various novel breakthroughs in digitalizing healthcare. The use of technology in improving …
SAGSleepNet: A deep learning model for sleep staging based on self-attention graph of polysomnography
Z Jin, K Jia - Biomedical Signal Processing and Control, 2023 - Elsevier
Sleep is crucial for human health. Automatic sleep stage classification based on
polysomnography (PSG) is meaningful for the diagnosis of sleep diseases, which has …
polysomnography (PSG) is meaningful for the diagnosis of sleep diseases, which has …
Learning product graphs from spectral templates
A Einizade, SH Sardouie - IEEE Transactions on Signal and …, 2023 - ieeexplore.ieee.org
Graph Learning (GL) is at the core of leveraging connections in machine learning (ML). By
observing a dataset of graph signals and considering specific assumptions, Graph Signal …
observing a dataset of graph signals and considering specific assumptions, Graph Signal …
4s-SleepGCN: Four-Stream Graph Convolutional Networks for Sleep Stage Classification
M Li, H Chen, Y Liu, Q Zhao - IEEE Access, 2023 - ieeexplore.ieee.org
Sleep staging serves as a critical basis for assessing sleep quality and diagnosing sleep
disorders in clinical practice. Most existing methods rely solely on a single channel for sleep …
disorders in clinical practice. Most existing methods rely solely on a single channel for sleep …
E2SGAN: EEG-to-SEEG translation with generative adversarial networks
High-quality brain signal data recorded by Stereoelectroencephalography (SEEG)
electrodes provide clinicians with clear guidance for presurgical assessments for epilepsy …
electrodes provide clinicians with clear guidance for presurgical assessments for epilepsy …
Cross-user electromyography pattern recognition based on a novel spatial-temporal graph convolutional network
With the goal of promoting the development of myoelectric control technology, this paper
focuses on exploring graph neural network (GNN) based robust electromyography (EMG) …
focuses on exploring graph neural network (GNN) based robust electromyography (EMG) …
Domain adaptation and generalization on functional medical images: A systematic survey
G Sarafraz, A Behnamnia, M Hosseinzadeh… - arXiv preprint arXiv …, 2022 - arxiv.org
Machine learning algorithms have revolutionized different fields, including natural language
processing, computer vision, signal processing, and medical data processing. Despite the …
processing, computer vision, signal processing, and medical data processing. Despite the …
A novel domain adversarial graph convolutional network for insulation defect diagnosis in gas-insulated substations
The data-driven models have achieved remarkable advancements in diagnosing gas-
insulated substation (GIS) insulation defects on specific massive datasets. However …
insulated substation (GIS) insulation defects on specific massive datasets. However …
Improved spatiotemporal information fusion approach based on bayesian decision theory for land cover classification
High-spatial-resolution (HSR) images and high-temporal-resolution (HTR) images have
their unique advantages and can be replenished by each other effectively. For land cover …
their unique advantages and can be replenished by each other effectively. For land cover …
A two-stream graph convolutional network based on brain connectivity for anesthetized states analysis
Investigating neural mechanisms of anesthesia process and developing efficient
anesthetized state detection methods are especially on high demand for clinical …
anesthetized state detection methods are especially on high demand for clinical …