FADLEC: feature extraction and arrhythmia classification using deep learning from electrocardiograph signals
Abstract Classifying arrhythmia is an essential step in the diagnosis and monitoring of cardiovascular illness. Deep learning (DL) models are trained on the electro-cardiogram recordings found in the ECG signal dataset to accurately classify arrhythmia into five groups: Normal (N), Fusion (F), Supra...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
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| Series: | Discover Artificial Intelligence |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44163-025-00290-0 |
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