Ibrahim, Mustafa Fuad RifetMustafa Fuad RifetIbrahimMeijer, MauriceMauriceMeijerSchlaefer, AlexanderAlexanderSchlaeferStelldinger, PeerPeerStelldinger2026-07-232026-07-232026-03th IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, IEEE PerCom Workshops 2026https://hdl.handle.net/11420/63977Continuous 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 (<=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.encontinuous patient monitoringecg analysisopen set recognitionout-of-distribution detectionunsupervised anomaly detectionwearable devicesTechnology::610: Medicine, HealthComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.31: Machine LearningTechnology::616: DiseasesConference Paper10.1109/PerComWorkshops68308.2026.11585248