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.

翻译：通过可穿戴设备进行连续心电图监测对于早期心血管疾病检测至关重要。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，特别是来自分布外病理状况和噪声的干扰。标准分类器在此类数据上常产生高置信度错误。现有的分布外检测方法要么忽略了计算约束，要么分别处理噪声和未见类别。本文研究将无监督异常检测作为一种轻量级的上游过滤机制。我们在严格的硬件约束下（参数≤512k），对六种UAD方法进行了神经架构搜索，包括深度支持向量数据描述、基于（变分）自编码器的输入重建、掩码异常检测、标准化流和去噪扩散概率模型，这些方法均适用于微控制器。在PTB-XL和BUT QDB数据集上的评估表明，经NAS优化的Deep SVDD在检测性能与模型大小之间提供了更优的帕累托效率。在模拟部署中，该轻量级滤波器将诊断分类器的准确率最高提升了21.0个百分点，证明优化的UAD滤波器能够有效保障可穿戴设备上的心电图分析。