Adaptive Model Selection for Real-Time Heart Disease Detection on Embedded Systems

Real-time cardiovascular disease (CVD) detection on wearable devices presents significant challenges due to the varying heart rate conditions and constrained computational capabilities of embedded systems. Existing approaches often struggle to balance diagnostic accuracy with the strict latency requirements imposed by different heart rate scenarios. In this study, we propose an Adaptive Model Selection (AMS) framework coupled with an anytime Convolutional Neural Network that integrates Residual Blocks, Squeeze-and-Excitation layers, and a Global Attention mechanism. By dynamically adjusting the model's complexity based on real-time heart rate, our solution optimizes diagnostic accuracy while ensuring a timely response. Evaluations conducted with the PhysioNet Database on a Raspberry Pi 4 demonstrate that our model achieves an accuracy of 91.5 % with an average inference latency of only 1.33 ms per sample. These outcomes illustrate the effectiveness and practical applicability of our framework for robust, responsive, and accurate on-device ECG monitoring in continuous cardiac care. Our code is available online on GitHub<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>https://github.com/yixinli19/AMS_CVD.