用轻量级异常检测提升可穿戴设备心电图分类的可靠性
Enhancing ECG Classification Robustness with Lightweight Unsupervised Anomaly Detection Filters
- 在微控制器上通过神经架构搜索优化无监督异常检测模型
- 在PTB-XL和BUT QDB数据集上提升诊断准确率最高达21.0个百分点
- 适合资源受限的可穿戴医疗设备部署，尤其关注噪声与未知病态
可穿戴设备持续心电图（ECG）监测对心血管疾病早期发现至关重要。然而，在资源受限的微控制器上部署深度学习模型面临可靠性挑战，尤其是面对分布外（OOD）病态和噪声时。标准分类器常在异常数据上产生高置信度错误。现有OOD检测方法或忽略计算约束，或分开处理噪声与未见类别。本文将无监督异常检测（UAD）作为轻量级前置过滤机制进行研究。我们在六种UAD方法（包括Deep SVDD、AE/VAE、MAD、NFs、DDPM）上执行神经架构搜索（NAS），在≤512k参数的严格硬件约束下进行优化，适用于微控制器。在PTB-XL和BUT QDB数据集上评估表明，经NAS优化的Deep SVDD在检测性能与模型大小之间具有最优帕累托效率。在模拟部署中，该轻量级过滤器使诊断分类器准确率最高提升21.0个百分点，证明优化后的UAD滤波器可有效保障可穿戴设备上的心电分析可靠性。
原文摘要 · Abstract (English)
Continuous electrocardiogram (ECG) monitoring via wearable devices is vital for early cardiovascular disease detection. However, deploying deep learning models on resource-constrained microcontrollers faces reliability challenges, particularly from Out-of-Distribution (OOD) pathologies and noise. Standard classifiers often yield high-confidence errors on such data. Existing OOD detection methods either neglect computational constraints or address noise and unseen classes separately. This paper investigates Unsupervised Anomaly Detection (UAD) as a lightweight, upstream filtering mechanism. We perform a Neural Architecture Search (NAS) on six UAD approaches, including Deep Support Vector Data Description (Deep SVDD), input reconstruction with (Variational-)Autoencoders (AE/VAE), Masked Anomaly Detection (MAD), Normalizing Flows (NFs) and Denoising Diffusion Probabilistic Models (DDPM) under strict hardware constraints ($\leq$512k parameters), suitable for microcontrollers. Evaluating on the PTB-XL and BUT QDB datasets, we demonstrate that a NAS-optimized Deep SVDD offers the superior Pareto efficiency between detection performance and model size. In a simulated deployment, this lightweight filter improves the accuracy of a diagnostic classifier by up to 21.0 percentage points, demonstrating that optimized UAD filters can safeguard ECG analysis on wearables.
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