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 …

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 …

Cloud-based training and edge-based inference modes for Artificial Intelligence of Medical
Things (AIoMT) applications suffer from accuracy degradation due to physiological signal …

Implementing Machine Learning (ML) models on Field-Programmable Gate Arrays (FPGAs)
is becoming increasingly popular across various domains as a low-latency and low-power …

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 …

Complexity measurement, essential in diverse fields like finance, biomedicine, climate
science, and network traffic, demands real-time computation to mitigate risks and losses …

The Multi-layer Perceptron (MLP) is a feedforward neural network commonly applied in
various artificial intelligence applications. Although fully parallelism can be exploited in this …

The primary diagnostic tool for monitoring problems with the heart or related diseases is the
electrocardiogram (ECG). The rising mortality rate caused by cardiac abnormalities leads to …