用可学习的节奏编码提升心电图疾病检测精度与可解释性
NeuroHD-RA: Neural-distilled Hyperdimensional Model with Rhythm Alignment
- 基于心律周期设计可训练编码器，动态对齐心脏节律
- 在Apnea-ECG上达73.09%精确率，F1值0.626，超越传统方法
- 兼具符号可解释性与边缘计算适配性，适合医疗健康监测
我们提出一种新颖且可解释的心电图疾病检测框架，融合超维计算（HDC）与可学习神经编码。不同于依赖静态随机投影的传统HDC方法，本方法基于RR区间设计一种节奏感知、可训练的编码流程，并采用符合心脏周期的生理信号分段策略。核心为神经蒸馏的HDC架构，包含可学习的RR块编码器和二值线性超维投影层，联合优化交叉熵与代理度量损失。该混合框架在保持HDC符号可解释性的同时，实现任务自适应表征学习。在Apnea-ECG和PTB-XL数据集上的实验表明，模型显著优于传统HDC与经典机器学习基线，在Apnea-ECG上达到73.09%精确率与0.626的F1分数，且在PTB-XL上表现稳健。该框架为边缘兼容的心电图分类提供高效可扩展方案，具有强可解释性与个性化健康监测潜力。
原文摘要 · Abstract (English)
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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