Balancing Accuracy and Energy Efficiency on Ultra-Law-Power Platforms for ECG Analysis

The widespread diffusion of long-term cardiac monitoring using wearable devices is a key opportunity for analyzing the health conditions of chronic patients. The continuous analysis of the heartbeat, even reduced to minimal configuration (i.e., two leads), can diagnose and keep track of many severe cardiac conditions, such as abnormal atrial and ventricular contractions. Since wearable devices are battery-powered, it is essential to design solutions that can improve the power efficiency of this monitoring, leveraging HW/SW optimization on low-power platforms. State-of-the-art algorithms based on advanced machine learning (ML) approaches achieve high accuracy but are extremely demanding in terms of energy consumption. In the context of battery-powered devices, determining a trade-off between accuracy and energy consumption is paramount to extending battery lifetime. This work presents a system design for analyzing the Electrocardiogram (ECG) signal to detect pathological conditions using an energy-efficient methodology based on Convolutional Neural Networks (CNNs). We assessed our solution on GAP9, a parallel microcontroller-class platform based on the RISC-V architecture. We achieved a 95.0% accuracy on the MIT-BIH Arrhythmia dataset, which includes five classes of pathological conditions. This value is marginally lower (3%) than the current state-of-the-art based on transformers. However, we identified the best energy-accuracy trade-off configuration, reducing the energy consumption of 3 x (0.03 mJ vs. 0.09 mJ) which guarantees a longer battery lifetime for critical applications.