用混合特征工程实现低资源心律失常检测，精度超98%且模型仅8.54KB
Optimized Hybrid Feature Engineering for Resource-Efficient Arrhythmia Detection in ECG Signals: An Optimization Framework
- 结合小波变换与图论中心性构建混合特征空间
- 在MIT-BIH和INCART数据集上达98.44%准确率，模型仅8.54KB
- 适合电池供电或无源心脏传感器等边缘设备部署
心血管疾病尤其是心律失常仍是全球主要致死原因，需通过物联网医疗（IoMT）实现持续监测。然而现有深度学习方法计算开销大，难以在资源受限的边缘设备上运行。本文提出一种以数据为中心的轻量级框架，强调特征工程而非模型复杂度。通过融合时频小波分解与图论结构描述符（如PageRank中心性），将高维心电数据转化为线性可分形式。该混合特征空间经互信息与递归剔除优化，支持可解释的超轻量级线性分类器。在MIT-BIH与INCART数据集上验证，诊断准确率达98.44%，模型仅占8.54KB。系统在每心跳52ms内完成分类，推理延迟0.46μs，实现实时处理。相较压缩模型如KD-Light（25KB，96.32%准确率），效率提升一个数量级，推动无源心脏传感器发展。
原文摘要 · Abstract (English)
Cardiovascular diseases, particularly arrhythmias, remain a leading global cause of mortality, necessitating continuous monitoring via the Internet of Medical Things (IoMT). However, state-of-the-art deep learning approaches often impose prohibitive computational overheads, rendering them unsuitable for resource-constrained edge devices. This study proposes a resource-efficient, data-centric framework that prioritizes feature engineering over complexity. Our optimized pipeline makes the complex, high-dimensional arrhythmia data linearly separable. This is achieved by integrating time-frequency wavelet decompositions with graph-theoretic structural descriptors, such as PageRank centrality. This hybrid feature space, combining wavelet decompositions and graph-theoretic descriptors, is then refined using mutual information and recursive elimination, enabling interpretable, ultra-lightweight linear classifiers. Validation on the MIT-BIH and INCART datasets yields 98.44% diagnostic accuracy with an 8.54 KB model footprint. The system achieves 0.46 $μ$s classification inference latency within a 52 ms per-beat pipeline, ensuring real-time operation. These outcomes provide an order-of-magnitude efficiency gain over compressed models, such as KD-Light (25 KB, 96.32% accuracy), advancing battery-less cardiac sensors.
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