FPGA Implementation of a Low-Power QRS Extractor

Among the bio-signals, the ECG is the most important waveform used for health analysis. It provides information about the heart rate, rhythm, and morphology of heart. Today, thanks to the development of advanced wearable devices, it is possible to track patient conditions outside hospital setting for several days. In such a context, the low power consumption becomes one of the crucial challenges in the development of wearable systems. In this paper, a low power implementation of Pan and Tompkins algorithm for QRS extraction is proposed. Results show that an appropriate hardware implementation significantly reduces the DSP portion power consumption of the algorithm compared with other implementation proposed in literature.

[1]  Fernando Boavida,et al.  Personal and Sensitive Data in the e-Health-IoT Universe , 2015, IoT 360.

[2]  Fabio Massimo Zanzotto,et al.  Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients , 2017, Medical decision making : an international journal of the Society for Medical Decision Making.

[3]  Ruby B. Lee,et al.  Integration of butterfly and inverse butterfly nets in embedded processors: Effects on power saving , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[4]  Gian Carlo Cardarilli,et al.  TDES cryptography algorithm acceleration using a reconfigurable functional unit , 2014, 2014 21st IEEE International Conference on Electronics, Circuits and Systems (ICECS).

[5]  H. K. Chatterjee,et al.  An FPGA implementation of real-time QRS detection , 2011, 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011).

[6]  M. Re,et al.  Implementation of the AES algorithm using a Reconfigurable Functional Unit , 2011, ISSCS 2011 - International Symposium on Signals, Circuits and Systems.

[7]  G.C. Cardarilli,et al.  Butterfly and Inverse Butterfly nets integration on Altera NIOS-II embedded processor , 2010, 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers.

[8]  Gian Carlo Cardarilli,et al.  Algorithm acceleration on LEON-2 processor using a reconfigurable bit manipulation unit , 2010, 2010 8th Workshop on Intelligent Solutions in Embedded Systems.

[9]  Jean-Yves Fourniols,et al.  Smart wearable systems: Current status and future challenges , 2012, Artif. Intell. Medicine.

[10]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[11]  Gian Carlo Cardarilli,et al.  Fine-grain Reconfigurable Functional Unit for embedded processors , 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[12]  Marco Tagliasacchi,et al.  An integrated system based on wireless sensor networks for patient monitoring, localization and tracking , 2013, Ad Hoc Networks.

[13]  Gian Carlo Cardarilli,et al.  Compressive Sensing Reconstruction for Complex System: A Hardware/Software Approach , 2016, ApplePies.

[14]  Mohammed Karim,et al.  NOVEL REAL-TIME FPGA-BASED QRS DETECTOR USING ADAPTIVE THRESHOLD WITH THE PREVIOUS SMALLEST PEAK OF ECG SIGNAL , 2013 .