A dual-encoder low-dimensional computing model classifies five AAMI ECG arrhythmia classes at 97.18% accuracy with 3.86 kB memory and zero DSP blocks on a Pynq-Z2 FPGA.
Global, regional, and national burden of cardiovascular diseases and risk factors in 204 countries and territories, 1990-2023,
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ECG-LDC: A Hardware-Efficient Low-Dimensional Computing Framework for ECG Arrhythmia Classification
A dual-encoder low-dimensional computing model classifies five AAMI ECG arrhythmia classes at 97.18% accuracy with 3.86 kB memory and zero DSP blocks on a Pynq-Z2 FPGA.