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ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors.

IEEE journal of biomedical and health informatics
Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenie...

Off-Body Sleep Analysis for Predicting Adverse Behavior in Individuals With Autism Spectrum Disorder.

IEEE journal of biomedical and health informatics
Poor sleep quality in Autism Spectrum Disorder (ASD) individuals is linked to severe daytime behaviors. This study explores the relationship between a prior night's sleep structure and its predictive power for next-day behavior in ASD individuals. Th...

Classification of Multi-Parametric Body MRI Series Using Deep Learning.

IEEE journal of biomedical and health informatics
Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrect information due to the sheer diversity of protocols and occasional t...

Modeling Functional Brain Networks for ADHD via Spatial Preservation-Based Neural Architecture Search.

IEEE journal of biomedical and health informatics
Modeling functional brain networks (FBNs) for attention deficit hyperactivity disorder (ADHD) has sparked significant interest since the abnormal functional connectivity is discovered in certain functional magnetic resonance imaging (fMRI)-based brai...

Framework for Deep Learning Based Multi-Modality Image Registration of Snapshot and Pathology Images.

IEEE journal of biomedical and health informatics
Multi-modality image registration is an important task in medical imaging because it allows for information from different domains to be correlated. Histopathology plays a crucial role in oncologic surgery as it is the gold standard for investigating...

Predicting Continuous Locomotion Modes via Multidimensional Feature Learning From sEMG.

IEEE journal of biomedical and health informatics
Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving ...

Characterizing the Contribution of Dependent Features in XAI Methods.

IEEE journal of biomedical and health informatics
Explainable Artificial Intelligence (XAI) provides tools to help understanding how AI models work and reach a particular decision or outcome. It helps to increase the interpretability of models and makes them more trustworthy and transparent. In this...

Multi-Loss Disentangled Generative-Discriminative Learning for Multimodal Representation in Schizophrenia.

IEEE journal of biomedical and health informatics
Schizophrenia (SCZ) is a multifactorial mental illness, thus it will be beneficial for exploring this disease using multimodal data, including functional magnetic resonance imaging (fMRI), genes, and the gut microbiome. Previous studies reported comb...

Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.

Neuroinformatics
The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compa...

Circulating endothelial progenitor cells and inflammatory markers in type 1 diabetes after an acute session of aerobic exercise.

Archives of endocrinology and metabolism
OBJECTIVE: To determine circulating endothelial progenitor cells (EPC) counts and levels of inflammatory markers in individuals with and without type 1 diabetes mellitus (T1DM) in response to an intense aerobic exercise session.