Conference Paper

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

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... In the wearable domain, we are targeting applications aimed at continuous monitoring of biomedical signals, which are the most energy-hungry. These applications usually work in a windowed fashion over long streams of data, and memory is the primary responsible for energy expenditure [10]. The objective in this experimental validation is to characterize the trade-off between the amount of input data that can be removed using our event-based approach, and the impact this has in the performance of the target task. ...
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Event-based sensors have the potential to optimize energy consumption at every stage in the signal processing pipeline, including data acquisition, transmission, processing and storage. However, almost all state-of-the-art systems are still built upon the classical Nyquist-based periodic signal acquisition. In this work, we design and validate the Polygonal Approximation Sampler (PAS), a novel circuit to implement a general-purpose event-based sampler using a polygonal approximation algorithm as the underlying sampling trigger. The circuit can be dynamically reconfigured to produce a coarse or a detailed reconstruction of the analog input, by adjusting the error threshold of the approximation. The proposed circuit is designed at the Register Transfer Level and processes each input sample received from the ADC in a single clock cycle. The PAS has been tested with three different types of archetypal signals captured by wearable devices (electrocardiogram, accelerometer and respiration data) and compared with a standard periodic ADC. These tests show that single-channel signals, with slow variations and constant segments (like the used single-lead ECG and the respiration signals) take great advantage from the used sampling technique, reducing the amount of data used up to 99% without significant performance degradation. At the same time, multi-channel signals (like the six-dimensional accelerometer signal) can still benefit from the designed circuit, achieving a reduction factor up to 80% with minor performance degradation. These results open the door to new types of wearable sensors with reduced size and higher battery lifetime.
... While the design of highly efficient microcontrollers and transmission devices has been the main focus of research from industry and academia in the last years, aiming for energy efficient modern wearable systems, sensor data acquisition performed by medical devices is still mostly based on the standard paradigm of regular signal sampling at the Nyquist rate. Some innovative proposals are emerging, for example to create event-based heart-rate analysis devices, such as in the work by Zanoli et al. [102], where the proposed approach is compared to the standard one, on the same ULP platform, providing a reduction of the energy consumption in runtime up to 15.6 times, while keeping almost the same detection performance. The traditional acquisition approach based on sampling at the Nyquist rate leads to data overload and an extra use of resources in the full processing pipeline, when applied to sparse and highly non-stationary signals, like those typically handled by medical devices. ...
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QRS detectors performance comparison in public databases
  • M Llamedo
  • J P Martínez
M. Llamedo and J. P. Martínez, "QRS detectors performance comparison in public databases," in Computing in Cardiology 2014, pp. 357-360, Sep. 2014.
Event-Based Control and Signal Processing
  • M Miskowicz
M. Miskowicz, Event-Based Control and Signal Processing. Embedded Systems, CRC Press, 2017.
Chapter 7 - ECG Signal Processing