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A Modified Algorithm for QRS Complex Detection for FPGA Implementation

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Abstract

This work is part of the Psypocket project which aims to conceive an embedded system able to recognize the stress state of an individual based on physiological and behavioural modifications. In this paper, one of the physiological data, the electrocardiographic (ECG) signal, is focused on. The QRS complex is the most significant segment in this signal. By detecting its position, the heart rate can be learnt. In this paper, a field-programmable gate array (FPGA) architecture for QRS complex detection is proposed. The detection algorithm adopts the integer Haar transform for ECG signal filtering and a maximum finding strategy to detect the location of R peak of the QRS complex. The ECG data are originally recorded by double-precision decimal with the sampling frequencies of 2000 Hz. For the FPGA implementation, they should be converted to integers with rounding operation. To find the best multiplying factor for rounding, the comparison is performed in MATLAB. Besides, to reduce the computation load in FPGA, the feasibility of the reduction in the sampling frequency is tested in MATLAB. The FPGA Cyclone EP3C5F256C6 is used as the target chip, and all the components of the system are implemented in VHSIC hardware description language. The testing results show that the proposed FPGA architecture achieves a high detection accuracy (98.41%) and a good design efficiency in terms of silicon consumption and operation speed. The proposed architecture will be adopted as a core unit to make a FPGA system for stress recognition.

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Authors and Affiliations

  1. Laboratory LCOMS, University of Lorraine, 7 rue Marconi, 57070, Metz, France

    Bo Zhang, Loïc Sieler, Yann Morère, Benoît Bolmont & Guy Bourhis

Authors
  1. Bo Zhang
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  2. Loïc Sieler
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  3. Yann Morère
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  4. Benoît Bolmont
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  5. Guy Bourhis
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Correspondence to Bo Zhang.

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Zhang, B., Sieler, L., Morère, Y. et al. A Modified Algorithm for QRS Complex Detection for FPGA Implementation. Circuits Syst Signal Process 37, 3070–3092 (2018). https://doi.org/10.1007/s00034-017-0711-6

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  • DOI: https://doi.org/10.1007/s00034-017-0711-6

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