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

TL;DR

This paper introduces NeuroHD-RA, a hybrid neural and hyperdimensional computing framework for ECG disease detection that uses rhythm-aware encoding and neural distillation to improve interpretability and performance.

Contribution

It presents a novel rhythm-aligned, trainable encoding pipeline combined with a neural-distilled HDC architecture for improved ECG classification.

Findings

Outperforms traditional HDC and classical ML baselines.

Achieves 73.09% precision and 0.626 F1 score on Apnea-ECG.

Demonstrates robustness on PTB-XL dataset.

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.