This paper comprehensively reviews hardware acceleration techniques and the deployment of convolutional neural networks (CNNs) for analyzing electroencephalogram (EEG) signals across various application areas, including emotion classification, motor ...
The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essenti...
IEEE journal of biomedical and health informatics
Sep 5, 2024
Ubiquitous sensing has been widely applied in smart healthcare, providing an opportunity for intelligent heart sound auscultation. However, smart devices contain sensitive information, raising user privacy concerns. To this end, federated learning (F...
IEEE journal of biomedical and health informatics
Sep 5, 2024
The ICU is a specialized hospital department that offers critical care to patients at high risk. The massive burden of ICU-requiring care requires accurate and timely ICU outcome predictions for alleviating the economic and healthcare burdens imposed...
IEEE journal of biomedical and health informatics
Sep 5, 2024
Electrocardiogram (ECG) is acquired during Magnetic Resonance Imaging (MRI) to monitor patients and synchronize image acquisition with the heart motion. ECG signals are highly distorted during MRI due to the complex electromagnetic environment. Autom...
IEEE journal of biomedical and health informatics
Sep 5, 2024
Due to their convenience, adhesive patch-type electrocardiographs are commonly used for arrhythmia screening. This study aimed to develop a reliable method that can improve the classification performance of atrial fibrillation (AF) using these device...
IEEE journal of biomedical and health informatics
Sep 5, 2024
Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynami...
IEEE journal of biomedical and health informatics
Sep 5, 2024
INTRODUCTION: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morph...
IEEE journal of biomedical and health informatics
Sep 5, 2024
Depression is a prevalent mental disorder that affects a significant portion of the global population. Despite recent advancements in EEG-based depression recognition models rooted in machine learning and deep learning approaches, many lack comprehen...
IEEE journal of biomedical and health informatics
Sep 5, 2024
Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops when coping with varying input modalities or unable to handle heterogeneous signals. To handle hetero...