An Attention-Based Deep Learning Approach for Sleep Stage Classification with Single-Channel EEG

Engineering, Electrical Engineering, Information Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology,
Attention   Deep Learning   Sleep Stage   Single-Channel EEG   Feature Extraction   Automatic Classification   Temporal Dependencies  

The IEEE “Transactions on Neural Systems and Rehabilitation Engineering” published a paper titled “Sleep Stage Classification Using Attention-Based Deep Learning for Single-Channel EEG” in Volume 29, 2021. The author of the article include Emadeldeen Edele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, Xiaoli Li, and Cuntai Guan. The main go...

GCTNet: A Graph Convolutional Transformer Network for Major Depressive Disorder Detection Based on EEG Signals

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Psychiatry, Neurology, Clinical Psychology, Medical Imaging,
Major Depressive Disorder   EEG   Deep Learning   Phase Locking Value   Contrastive Learning  

GCTNet: Graph Convolution Transformer Network for Detecting Major Depressive Disorder Based on EEG Signals Research Background Major Depressive Disorder (MDD) is a prevalent mental illness characterized by significant and persistent low mood, affecting over 350 million people worldwide. MDD is one of the leading causes of suicide, resulting in appr...

Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstruction

Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstruction

Information Science, Artificial Intelligence, Medicine, Medical Imaging,
Low Dose CT   Noise-Generating Mechanism   Implicit Regularization   Full-Domain Optimization   Deep Learning  

Application of Implicit Regularization Learning Network Based on Noise Generation and Imaging Mechanisms in Low-Dose CT Reconstruction Low-Dose Computed Tomography (LDCT) has become an important tool in modern medical imaging, aiming to reduce radiation risks while maintaining image quality. However, reducing the X-ray dose often leads to data corr...

Unsupervised Fusion of Misaligned PAT and MRI Images via Mutually Reinforcing Cross-Modality Image Generation and Registration

Information Science, Artificial Intelligence, Medicine, Medical Imaging,
Photoacoustic Tomography   Magnetic Resonance Imaging   Image Fusion   Multimodal Registration   Deep Learning  

Unsupervised Fusion of Unaligned PAT and MRI Images Using Mutually Enhancing Cross-Modality Image Generation and Registration Methods Background and Research Objectives In recent years, photoacoustic tomography (PAT) and magnetic resonance imaging (MRI) have been widely used in preclinical research as cutting-edge biomedical imaging techniques. PAT...

Heart Sound Abnormality Detection from Multi-Institutional Collaboration: Introducing a Federated Learning Framework

Heart Sound Abnormality Detection from Multi-Institutional Collaboration: Introducing a Federated Learning Framework

Information Science, Artificial Intelligence, Medicine, Cardiovascular System,
Heart Sound Abnormality   Multi-Institutional Collaboration   Federated Learning   Privacy Protection   Deep Learning  

Academic Background Cardiovascular diseases (CVDs) have become one of the leading causes of death, particularly within the elderly population, making cardiovascular health a pressing societal concern. Early screening, diagnosis, and prognosis management are crucial for preventing hospitalizations. Heart sound signals carry rich physiological and pa...