# ECG-LDC: A Hardware-Efficient Low-Dimensional Computing Framework for ECG Arrhythmia Classification

arXiv: https://arxiv.org/abs/2607.09680 · HTML: https://mlsanity.com/paper/2607.09680/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 13 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices](https://mlsanity.com/paper/2203.04894/) (2022)
- [A Tiny Transformer for Low-Power Arrhythmia Classification on Microcontrollers](https://mlsanity.com/paper/2402.10748/) (2024)
- [An adaptive cognitive sensor node for ECG monitoring in the Internet of Medical Things](https://mlsanity.com/paper/2106.06498/) (2021)
- [Arrhythmia Classifier Based on Ultra-Lightweight Binary Neural Network](https://mlsanity.com/paper/2304.01568/) (2023)
- [Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices](https://mlsanity.com/paper/2205.03661/) (2022)
- [Multi-Feature Fusion and Compressed Bi-LSTM for Memory-Efficient Heartbeat Classification on Wearable Devices](https://mlsanity.com/paper/2405.15312/) (2024)
- [Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture](https://mlsanity.com/paper/2502.14075/) (2025)
- [Dense Neural Network Based Arrhythmia Classification on Low-cost and Low-compute Micro-controller](https://mlsanity.com/paper/2504.03531/) (2025)

## Abstract (arXiv)

Continuous cardiac monitoring in wearable devices demands classifiers that are simultaneously accurate, energy-efficient, and deployable on resource-constrained hardware. While deep neural network approaches have demonstrated high classification accuracy for electrocardiogram (ECG) arrhythmia detection, their substantial parameter counts and reliance on multiply-accumulate-intensive operations make them impractical for low-cost edge platforms. In this work, we propose ECG-LDC, a hardware-software co-design framework that adapts Low-Dimensional Computing (LDC) for real-time ECG arrhythmia classification. ECG-LDC employs a dual-encoder architecture with dedicated value and feature codebooks to independently encode morphological waveform features and RR-interval temporal features, enabling effective capture of both intra-beat and inter-beat cardiac dynamics. The framework encompasses data preprocessing, model training, and a hardware accelerator architecture prototyped on the Pynq-Z2 platform. Implemented using binary representations and XOR/XNOR-based operations, ECG-LDC achieves 97.18% accuracy with a memory footprint of only 3.86 kB. ECG-LDC sacrifices approximately 1.8% accuracy versus SOTA TinyML classifiers but achieves 11~ 570× reduction in memory usage; among FPGA-based five-class arrhythmia classifiers, it delivers the highest accuracy with up to 2.4× fewer LUTs and zero DSP block utilization, affirming its suitability for real-time arrhythmia detection on resource-constrained wearable platforms.