An on-board executable multi-feature transfer-enhanced fusion model for three-lead eeg sensor-assisted depression diagnosis
The development of affective computing and medical electronic technologies has led to the
emergence of Artificial Intelligence (AI)-based methods for the early detection of depression …
emergence of Artificial Intelligence (AI)-based methods for the early detection of depression …
Innovations in Quantitative Rapid Testing: Early Prediction of Health Risks
As health monitoring becomes increasingly intricate, the demand for innovative solutions to
predict and assess health status is more pressing than ever. This review focuses on the …
predict and assess health status is more pressing than ever. This review focuses on the …
rule4ml: An open-source tool for resource utilization and latency estimation for ML models on FPGA
Abstract Implementing Machine Learning (ML) models on Field-Programmable Gate Arrays
(FPGAs) is becoming increasingly popular across various domains as a low-latency and low …
(FPGAs) is becoming increasingly popular across various domains as a low-latency and low …
A prediction method of diabetes comorbidity based on non-negative latent features
L Zhou, K Liu, Y Wang, H Qin, T He - Neurocomputing, 2024 - Elsevier
In this paper, we present a novel network-based approach, namely Inherently Non-negative
Latent Feature Analysis for Diabetes Mellitus Comorbidity Detection (INDM), to enhance the …
Latent Feature Analysis for Diabetes Mellitus Comorbidity Detection (INDM), to enhance the …
HybMED: A Hybrid Neural Network Training Processor with Multi-Sparsity Exploitation for Internet of Medical Things
Cloud-based training and edge-based inference modes for Artificial Intelligence of Medical
Things (AIoMT) applications suffer from accuracy degradation due to physiological signal …
Things (AIoMT) applications suffer from accuracy degradation due to physiological signal …
[HTML][HTML] Fast Resource Estimation of FPGA-Based MLP Accelerators for TinyML Applications
Tiny machine learning (TinyML) demands the development of edge solutions that are both
low-latency and power-efficient. To achieve these on System-on-Chip (SoC) FPGAs, co …
low-latency and power-efficient. To achieve these on System-on-Chip (SoC) FPGAs, co …
Recent advances in the tools and techniques for AI-aided diagnosis of atrial fibrillation
Atrial fibrillation (AF) is recognized as a develo** global epidemic responsible for a
significant burden of morbidity and mortality. To counter this public health crisis, the …
significant burden of morbidity and mortality. To counter this public health crisis, the …
Acceleration of Bucket-Assisted Fast Sample Entropy for Biomedical Signal Analysis
Sample entropy (SampEn) is widely used to assess the complexity of physiological time-
series signals. However, it is a computationally intensive algorithm with time complexity …
series signals. However, it is a computationally intensive algorithm with time complexity …
Acceleration of Fast Sample Entropy for FPGAs
Complexity measurement, essential in diverse fields like finance, biomedicine, climate
science, and network traffic, demands real-time computation to mitigate risks and losses …
science, and network traffic, demands real-time computation to mitigate risks and losses …
The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment
H Liang, H Zhang, J Wang, X Shao… - Reviews in …, 2024 - pmc.ncbi.nlm.nih.gov
Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although the guidelines for
AF have been updated in recent years, its gradual onset and associated risk of stroke pose …
AF have been updated in recent years, its gradual onset and associated risk of stroke pose …