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Precision-guided gramian angular field for ECG-based cardiac arrhythmia detection utilizing binary-weight simplicial convolutional neural networks
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DOI：10.1016/j.bspc.2026.111131.png)
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Abstract
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Cardiac arrhythmia, characterized by abnormal heart rhythms, can be effectively identified using electrocardiogram (ECG) signals. However, the complex temporal and frequency patterns in ECG recordings make accurate diagnosis challenging. Recent advances in deep learning have significantly improved automated arrhythmia detection. This paper proposes a Precision-Guided Gramian Angular Field–based ECG classification framework utilizing Binary-Weight Simplicial Convolutional Neural Networks (PGDWF-BWSCNN).
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