Atrial Fibrillation Detection on the Embedded Edge: Energy-Efficient Inference on a Low-Power Microcontroller

Atrial Fibrillation (AF) is a common yet often undiagnosed cardiac arrhythmia with serious clinical consequences, including increased risk of stroke, heart failure, and mortality. In this work, we present a novel Embedded Edge system performing real-time AF detection on a low-power Microcontroller Unit (MCU). Rather than relying on full Electrocardiogram (ECG) waveforms or cloud-based analytics, our method extracts Heart Rate Variability (HRV) features from RR-Interval (RRI) and performs classification using a compact Long Short-Term Memory (LSTM) model optimized for embedded deployment. We achieved an overall classification accuracy of 98.46% while maintaining a minimal resource footprint: inference on the target MCU completes in 143 ± 0 ms and consumes 3532 ± 6 μJ per inference. This low power consumption for local inference makes it feasible to strategically keep wireless communication OFF, activating it only to transmit an alert upon AF detection, thereby reinforcing privacy and enabling long-term battery life. Our results demonstrate the feasibility of performing clinically meaningful AF monitoring directly on constrained edge devices, enabling energy-efficient, privacy-preserving, and scalable screening outside traditional clinical settings. This work contributes to the growing field of personalised and decentralised cardiac care, showing that Artificial Intelligence (AI)-driven diagnostics can be both technically practical and clinically relevant when implemented at the edge.