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

• URL: http://arxiv.org/abs/2607.09680v1
• Date: Sat, 13 Jun 2026 07:47:28 GMT
• Title: ECG-LDC: A Hardware-Efficient Low-Dimensional Computing Framework for ECG Arrhythmia Classification
• Abstract summary: Deep neural network approaches have demonstrated high classification accuracy for electrocardiogram (ECG) arrhythmia detection.<n>Deep neural network approaches have substantial parameter counts and reliance on multiply-accumulate-intensive operations make them impractical for low-cost edge platforms.<n>We propose ECG-LDC, a hardware-software co-design framework that adapts Low-Dimensional Computing for real-time ECG arrhythmia classification.
• Score: 7.825539979512302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $3.86\ \text{kB}$. ECG-LDC sacrifices approximately $1.8\%$ accuracy versus SOTA TinyML classifiers but achieves $11$~$ 570\times$ reduction in memory usage; among FPGA-based five-class arrhythmia classifiers, it delivers the highest accuracy with up to $2.4\times$ fewer LUTs and zero DSP block utilization, affirming its suitability for real-time arrhythmia detection on resource-constrained wearable platforms.

Related papers

• ECG-NAT: A Self-supervised Neighborhood Attention Transformer for Multi-lead Electrocardiogram Classification [4.904219291349455]
  ECG signals require architectures that simultaneously capture fine-grained beat-level morphology and broader rhythm-level dependencies.<n>This paper proposes the Electrocardiogram Neighborhood Attention Transformer (ECG-NAT), a novel self-supervised learning approach tailored for ECG classification.<n>ECG-NAT achieves robust performance on benchmark datasets, with 88.1% accuracy using only 1% labeled data.
  arXiv  Detail & Related papers  (2026-05-13T08:50:00Z)
• From Foundation ECG Models to NISQ Learners: Distilling ECGFounder into a VQC Student [0.0]
  We fine-tune ECGFounder as a high-capacity teacher for binary ECG classification on PTB-XL and the MIT-BIH Arrhythmia Database.<n>We investigate whether knowledge distillation can transfer its predictive behavior to compact students.
  arXiv  Detail & Related papers  (2026-03-28T13:36:45Z)
• Echo2ECG: Enhancing ECG Representations with Cardiac Morphology from Multi-View Echos [53.368813255127115]
  Existing self-supervised methods suffer from a representational mismatch by aligning ECGs to single-view Echos.<n>We propose Echo2ECG, a multimodal self-supervised learning framework that enriches ECG representations with the heart's morphological structure captured in multi-view Echos.<n>We evaluate Echo2ECG as an ECG feature extractor on two clinically relevant tasks.
  arXiv  Detail & Related papers  (2026-03-09T15:39:57Z)
• Optimized Hybrid Feature Engineering for Resource-Efficient Arrhythmia Detection in ECG Signals: An Optimization Framework [0.0]
  This study proposes a resource-efficient, data-centric framework that prioritizes feature engineering over complexity.<n>Our optimized pipeline makes the complex, high-dimensional arrhythmia data linearly separable.<n> validation on the MIT-BIH and INCART datasets yields 98.44% diagnostic accuracy with an 8.54 KB model footprint.
  arXiv  Detail & Related papers  (2026-01-01T03:44:42Z)
• ArrhythmiaVision: Resource-Conscious Deep Learning Models with Visual Explanations for ECG Arrhythmia Classification [0.0]
  We propose ArrhythmiNet V1 and V2, optimized for efficient, real-time arrhythmia classification on edge devices.<n>Inspired by MobileNet's depthwise separable convolutional design, these models maintain memory footprints of just 302.18 KB and 157.76 KB, respectively.<n>Our findings demonstrate the feasibility of combining interpretability, predictive accuracy, and computational efficiency in practical, wearable, and embedded ECG monitoring systems.
  arXiv  Detail & Related papers  (2025-04-30T18:22:45Z)
• GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images [44.50428701650495]
  We introduce GEM, the first MLLM unifying ECG time series, 12-lead ECG images and text for grounded and clinician-aligned ECG interpretation.<n> GEM enables feature-grounded analysis, evidence-driven reasoning, and a clinician-like diagnostic process through three core innovations.<n>We propose the Grounded ECG task, a clinically motivated benchmark designed to assess the MLLM's capability in grounded ECG understanding.
  arXiv  Detail & Related papers  (2025-03-08T05:48:53Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• PulseNet: Deep Learning ECG-signal classification using random augmentation policy and continous wavelet transform for canines [46.09869227806991]
  evaluating canine electrocardiograms (ECG) require skilled veterinarians. Current availability of veterinary cardiologists for ECG interpretation and diagnostic support is limited. We implement a deep convolutional neural network (CNN) approach for classifying canine electrocardiogram sequences as either normal or abnormal.
  arXiv  Detail & Related papers  (2023-05-17T09:06:39Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms [9.884633954053344]
  Deep learning can be used to achieve personal authentication in biometric security applications. We developed a model for the detection of arrhythmia in which time-sliced ECG data represents the distance between successive R-peaks. This compact system can be implemented in wearable devices or real-time monitoring equipment.
  arXiv  Detail & Related papers  (2020-12-01T09:10:24Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.