NeuroHD-RA: Neural-distilled Hyperdimensional Model with Rhythm Alignment

• URL: http://arxiv.org/abs/2507.14184v3
• Date: Wed, 23 Jul 2025 05:51:42 GMT
• Title: NeuroHD-RA: Neural-distilled Hyperdimensional Model with Rhythm Alignment
• Authors: ZhengXiao He, Jinghao Wen, Huayu Li, Siyuan Tian, Ao Li,
• Abstract summary: We present a novel and interpretable framework for electrocardiogram (ECG)-based disease detection.<n>We introduce a rhythm-aware and trainable encoding pipeline based on RR intervals, a physiological signal segmentation strategy.<n>Our framework offers an efficient and scalable solution for edge-compatible ECG classification, with strong potential for interpretable and personalized health monitoring.
• Score: 1.1514907665987528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel and interpretable framework for electrocardiogram (ECG)-based disease detection that combines hyperdimensional computing (HDC) with learnable neural encoding. Unlike conventional HDC approaches that rely on static, random projections, our method introduces a rhythm-aware and trainable encoding pipeline based on RR intervals, a physiological signal segmentation strategy that aligns with cardiac cycles. The core of our design is a neural-distilled HDC architecture, featuring a learnable RR-block encoder and a BinaryLinear hyperdimensional projection layer, optimized jointly with cross-entropy and proxy-based metric loss. This hybrid framework preserves the symbolic interpretability of HDC while enabling task-adaptive representation learning. Experiments on Apnea-ECG and PTB-XL demonstrate that our model significantly outperforms traditional HDC and classical ML baselines, achieving 73.09\% precision and an F1 score of 0.626 on Apnea-ECG, with comparable robustness on PTB-XL. Our framework offers an efficient and scalable solution for edge-compatible ECG classification, with strong potential for interpretable and personalized health monitoring.

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