ArticleSensors (Basel, Switzerland)2024
A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Deep Learning-Based Multi-Lead ECG Reconstruction from Lead I with Metadata Integration and Uncertainty Estimation.Sensors (Basel, Switzerland) · 2025Article
- Real-Time Heartbeat Classification on Distributed Edge Devices: A Performance and Resource Utilization Study.Sensors (Basel, Switzerland) · 2025Article
- New Method for Tomato Disease Detection Based on Image Segmentation and Cycle-GAN Enhancement.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics.
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Registered trials
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