DeepECG-Net: a hybrid transformer-based deep learning model for real-time ECG anomaly detection

DeepECG-Net: a hybrid transformer-based deep learning model for real-time ECG anomaly detection

Abstract Real-time Electrocardiogram (ECG) anomaly detection is critical for accurate diagnosis and timely intervention in cardiac disorders. Existing models, such as CNNs and LSTMs, often struggle with long-range dependencies, generalization across multiple ECG patterns, and real-time inference wit...

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Bibliographic Details
Main Author: Manal Alghieth
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Real-time ECG anomaly detection
Hybrid transformer-CNN model
DeepECG-Net
Global dependency learning
Efficient deep learning framework
Online Access:https://doi.org/10.1038/s41598-025-07781-1
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https://doi.org/10.1038/s41598-025-07781-1

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