Published March 9, 2020 | Version v1
Conference paper Open

An Event-Based System for Low-Power ECG QRS Complex Detection

  • 1. EPFL
  • 2. University of Bologna


One of the greatest challenges in the design of modern wearable devices is energy efficiency. While data processing and communication have received a lot of attention from the industry and academia, leading to highly efficient microcontrollers and transmission devices, sensor data acquisition in medical devices is still based on a conservative paradigm that requires regular sampling at the Nyquist rate of the target signal. This requirement is usually excessive for signals that are typically sparse and highly non-stationary, leading to data overload and a waste of resources in the full processing pipeline. In this work we propose a new system to create event-based heart-rate analysis devices, including a novel algorithm for QRS detection that is able to process electrocardiogram signals acquired irregularly and much below the theoretically-required Nyquist rate. This technique allows us to drastically reduce the average sampling frequency of the signal and, hence, the energy needed to process it and extract the relevant information. We implemented both the proposed event-based algorithm and a state-of-the-art version based on regular sampling on an ultra-low power hardware platform, and the experimental results show that the event-based version reduces the energy consumption in runtime up to 15.6 times, while the detection performance is maintained at an average F1 score of 99.5%.


EPFL - An Event-Based System for Low-Power ECG QRS Complex Detection_Preprint.pdf

Additional details


DeepHealth – Deep-Learning and HPC to Boost Biomedical Applications for Health 825111
European Commission
HBP SGA2 – Human Brain Project Specific Grant Agreement 2 785907
European Commission
ML-edge: Enabling Machine-Learning-Based Health Monitoring in Edge Sensors via Architectural Customization 200020_182009
Swiss National Science Foundation