FPGA-based system for heart rate monitoring

The continuous monitoring of cardiac patients requires an ambulatory system that can automatically detect heart diseases. This study presents a new field programmable gate array (FPGA)-based hardware implementation of the QRS complex detection. The proposed detection system is mainly based on the Pan and Tompkins algorithm, but applying a new, simple, and efficient technique in the detection stage. The new method is based on the centred derivative and the intermediate value theorem, to locate the QRS peaks. The proposed architecture has been implemented on FPGA using the Xilinx System Generator for digital signal processor and the Nexys-4 FPGA evaluation kit. To evaluate the effectiveness of the proposed system, a comparative study has been performed between the resulting performances and those obtained with existing QRS detection systems, in terms of reliability, execution time, and FPGA resources estimation. The proposed architecture has been validated using the 48 half-hours of records obtained from the Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH) arrhythmia database. It has also been validated in real time via the analogue discovery device.

[1]  Andrej Zemva,et al.  Hardware Implementation of a Modified Delay-Coordinate Mapping-Based QRS Complex Detection Algorithm , 2007, EURASIP J. Adv. Signal Process..

[2]  Rajarshi Gupta,et al.  Real-time detection of electrocardiogram wave features using template matching and implementation in FPGA , 2015 .

[3]  Zhi Xu,et al.  A novel approach to phase space reconstruction of single lead ECG for QRS complex detection , 2018, Biomed. Signal Process. Control..

[4]  Willis J. Tompkins,et al.  Quantitative Investigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Database , 1986, IEEE Transactions on Biomedical Engineering.

[5]  H. K. Chatterjee,et al.  Real Time P and T Wave Detection from Ecg using Fpga , 2012 .

[6]  Patrick Gaydecki,et al.  The use of the Hilbert transform in ECG signal analysis , 2001, Comput. Biol. Medicine.

[7]  Ivaylo I Christov,et al.  Real time electrocardiogram QRS detection using combined adaptive threshold , 2004, Biomedical engineering online.

[8]  Reza Lotfi,et al.  A Level-Crossing Based QRS-Detection Algorithm for Wearable ECG Sensors , 2014, IEEE Journal of Biomedical and Health Informatics.

[9]  Guy Bourhis,et al.  A Modified Algorithm for QRS Complex Detection for FPGA Implementation , 2017, Circuits, Systems, and Signal Processing.

[10]  Zohreh Azimifar,et al.  Chaotic based reconstructed phase space features for detecting ventricular fibrillation , 2010, Biomed. Signal Process. Control..

[11]  R. Orglmeister,et al.  The principles of software QRS detection , 2002, IEEE Engineering in Medicine and Biology Magazine.

[12]  Zhongjie Hou,et al.  A Real-Time QRS Detection Method Based on Phase Portraits and Box-Scoring Calculation , 2018, IEEE Sensors Journal.

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

[14]  Derya Avci,et al.  A new technique for ECG signal classification genetic algorithm Wavelet Kernel extreme learning machine , 2019, Optik.

[15]  Emine Dogru Bolat,et al.  An improved QRS complex detection method having low computational load , 2018, Biomed. Signal Process. Control..

[16]  Ashish Kumar,et al.  Design of high performance QRS complex detector for wearable healthcare devices using biorthogonal spline wavelet transform. , 2018, ISA transactions.

[17]  Shubhajit Roy Chowdhury,et al.  Field Programmable Gate Array Based Fuzzy Neural Signal Processing System for Differential Diagnosis of QRS Complex Tachycardia and Tachyarrhythmia in Noisy ECG Signals , 2012, Journal of Medical Systems.

[18]  Adel Belouchrani,et al.  QRS detection based on wavelet coefficients , 2012, Comput. Methods Programs Biomed..

[19]  Feng Wan,et al.  A 0.83-$\mu {\rm W}$ QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35- $\mu{\rm m}$ CMOS , 2012, IEEE Transactions on Biomedical Circuits and Systems.

[20]  Stéphane Mallat,et al.  Singularity detection and processing with wavelets , 1992, IEEE Trans. Inf. Theory.

[21]  Rosaria Silipo,et al.  Artificial neural networks for automatic ECG analysis , 1998, IEEE Trans. Signal Process..

[22]  Enrique Romero,et al.  ECG assessment based on neural networks with pretraining , 2016, Appl. Soft Comput..

[23]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[24]  Manu Thomas,et al.  Automatic ECG arrhythmia classification using dual tree complex wavelet based features , 2015 .

[25]  Andrej Zemva,et al.  FPGA-oriented HW/SW implementation of ECG beat detection and classification algorithm , 2010, Digit. Signal Process..

[26]  Pornchai Phukpattaranont,et al.  QRS detection algorithm based on the quadratic filter , 2015, Expert Syst. Appl..

[27]  Chi-Sang Poon,et al.  Analysis of First-Derivative Based QRS Detection Algorithms , 2008, IEEE Transactions on Biomedical Engineering.

[28]  Sjaak Brinkkemper,et al.  Efficiency of Clinical Decision Support Systems Improves with Experience , 2016, Journal of Medical Systems.

[29]  Sanjay Ranka,et al.  Machine learning approaches for predicting high cost high need patient expenditures in health care , 2018, BioMedical Engineering OnLine.

[30]  Kamsali Manjunatha Chari,et al.  Efficient FPGA-based VLSI architecture for detecting R-peaks in electrocardiogram signal by combining Shannon energy with Hilbert transform , 2018, IET Signal Process..

[31]  R. Stojanović,et al.  A FPGA system for QRS complex detection based on Integer Wavelet Transform , 2011 .

[32]  Madhuchhanda Mitra,et al.  Empirical mode decomposition based ECG enhancement and QRS detection , 2012, Comput. Biol. Medicine.