Automated arrhythmia detection from electrocardiogram (ECG) signals is crucial and important for the early treatment of cardiac disease (CD). In this investigation, eight machine-learning models have been developed to identify improved ECG arrhythmia...
Cardiovascular disease (CVD) is the top cause of mortality globally, making it crucial to diagnose arrhythmias promptly and accurately for the early prevention and treatment of CVD. While numerous methods exist for detecting arrhythmias using ECG sig...
Biomedical physics & engineering express
Oct 15, 2025
Electrocardiogram (ECG) is essential for assessing heart function, but manual analysis is time-consuming and error-prone. Automated ECG analysis can improve early detection of cardiovascular diseases by accurately identifying abnormal beats despite s...
Biomedical physics & engineering express
Oct 14, 2025
This study details the development of a remote patient monitoring system with a primary focus on a novel, customized Deep Neural Network (DNN) for arrhythmia detection. The system integrates hardware for real-time data collection from biomedical sens...
This study introduces a robust and efficient hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BLSTM) networks for the automated detection and classification of cardiac arrhy...
International journal of neural systems
Aug 27, 2025
In this paper, we present a hybrid learning framework that integrates two model-driven AI paradigms: Deep unfolding and Variable Projections (VPs). The core idea is to unfold the iterations of VP solvers for separable nonlinear least squares (SNLLS) ...
This study explores how topological indices (TIs), which are mathematical descriptors of a drug's molecular structure, can support to predict vital properties and biological activities. This understanding is a key for more effective drug design. We f...
Accurate diagnosis of heart arrhythmias requires the interpretation of electrocardiograms (ECG), which capture the electrical activity of the heart. Automating this process through machine learning is challenging due to the need for large annotated d...
Detection of Cardiovascular Diseases (CVDs) has become crucial nowadays, as the World Health Organization (WHO) declares CVDs as the major leading causes of death in the globe. Moreover, the death rate due to CVDs is expected to rise in the next few ...
This paper presents a novel privacy-preserving architecture, a fusion of Federated Learning with Personalized Models and Differential Privacy (FLPMDP), for diagnosing arrhythmia from 12-lead electrocardiogram (ECG) signals. The architecture supports ...
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