ArticleScientific reports2024
Multimodal ECG heartbeat classification method based on a convolutional neural network embedded with FCA.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- EYE-ECG2: Addressing medical student feedback in an RCT with eye-tracking videos featuring cued retrospective reporting and modified learning sequences for ECG interpretation skills training.GMS journal for medical education · 2025Trial
- A lightweight deep neural network for personalized detecting ventricular arrhythmias from a single-lead ECG device.PLOS digital health · 2025Article
- Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks.Scientific reports · 2025Article
- A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Arrhythmias are irregular heartbeat rhythms caused by various conditions. Automated ECG signal classification aids in diagnosing and predicting arrhythmias. Current studies mostly focus on 1D ECG signals, overlooking the fusion of multiple ECG modalities for enhanced analysis. We converted ECG signals into modal images using RP, GAF, and MTF, inputting them into our classification model. To optimize detail retention, we introduced a CNN-based model with FCA for multimodal ECG tasks. Achieving 99.6% accuracy on the MIT-BIH arrhythmia database for five arrhythmias, our method outperforms prior models. Experimental results confirm its reliability for ECG classification tasks.
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Registered trials
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