Deep learning for personalized electrocardiogram diagnosis: A review
The electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its
interpretation traditionally reliant on the expertise of cardiologists. The emergence of deep …
interpretation traditionally reliant on the expertise of cardiologists. The emergence of deep …
Review on spiking neural network-based ECG classification methods for low-power environments
This paper reviews arrhythmia classification studies using electrocardiogram (ECG) signals.
Research on automatically diagnosing arrhythmia in daily life has been actively underway …
Research on automatically diagnosing arrhythmia in daily life has been actively underway …
Enhancing Terahertz Spectral Recognition of Lung Cancer Cells Through Synthetic Signal Generation
Deep learning-based medical diagnosis models heavily rely on large-scale datasets
encompassing diverse aspects of a patient's condition. However, the scarcity of such …
encompassing diverse aspects of a patient's condition. However, the scarcity of such …
Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix
S Han - 2024 IEEE International Conference on Bioinformatics …, 2024 - ieeexplore.ieee.org
With the advancements in graph neural network (GNN), there has been increasing interest
in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an …
in applying GNN to electrocardiogram (ECG) analysis. In this study, we generated an …
An overview of secure authentication methods using ECG biometrics with deep learning algorithms
N Mokhtari, A Safari, S Sadeghi - Biannual Journal Monadi for …, 2023 - monadi.isc.org.ir
Biometric systems are an important technique for user identification in today's world, which
have been welcomed due to their non-invasive nature and high resistance to forgery and …
have been welcomed due to their non-invasive nature and high resistance to forgery and …