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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 sparse and highly non-stationary signals, 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 …