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m 5151117 Yumiko Kimezawa

An FPGA implementation of real-time QRS detection H.K.Chatterjee Dept. of ECE Camellia School of Engineering & Technology Kolkata India R.Gupta , J.N.Bera , M.Mitra Dept. of Applied Physics University Of Calcutta Kolkata India. m 5151117 Yumiko Kimezawa. Outline. Introduction

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m 5151117 Yumiko Kimezawa

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  1. RPR An FPGA implementation ofreal-time QRS detectionH.K.Chatterjee Dept. of ECECamellia School of Engineering & TechnologyKolkata IndiaR.Gupta, J.N.Bera, M.MitraDept. of Applied PhysicsUniversity Of Calcutta Kolkata India m5151117 Yumiko Kimezawa

  2. Outline RPR • Introduction • Materials and Methods • Testing and Results • Conclusion

  3. Introduction RPR • QRS detection is one of the important and primary job and very often used for heart rate computation • In recent years, there has been considerable use of FPGA based system for ECG monitoring, QRS detection and feature extraction • This paper illustrates a real time QRS detection algorithm using an FPGA based embedded system

  4. Materials and Methods RPR Data Port Data Capture & Analysis Section Display Section Status Port Trigger pulse train Control Port Start Capture FPGA Xilinx Spartan 2 Parallel port 8 Switches ptb-db file 8 LEDs PC Interfacing Unit Figure 1: Block diagram of the system

  5. Materials and Methods RPR • The entire work • Generation of digitized ECG from ptb-db file • Development and testing of the algorithm in FPGA platform

  6. Materials and Methods RPR • Generation of digitized ECG from ptb-dbfile Data send from PC parallel port (D0 – D7) Data accepted by FPGA (P108 – 111, P113 – 115, P119) Pulse train generated by FPGA (P121), & accepted by PC parallel port (S7) “Start Capture” pulse generated by PC parallel port (C0) & accepted by FPGA (P120) Figure 2: Generation of ECG data by PC

  7. Materials and Methods RPR • Real time QRS detection from the ECG samples • The training zone: The first 1500 samples • A characterization of QRS polarization is performed based on 20 point slope • ECG samples are stored in a group of memory cells which holds the last 42 samples • Computing 20 point average slope by calculation differences like R20-R19, R19-R18,….., R2-R1 Formula:

  8. Materials and Methods RPR Current index point R42 R20 R1 Current point of reference Current index point R20 Group I: Left side 20 pt. slope: R42-R21 > 0 & Right side 20 pt. slope: R20-R1 < 0 Group II: Left side 20 pt. slope: R42-R21 < 0 & Right side 20 pt. slope: R20-R1 > 0 Average of both side slope: (|R20-R40| + |R21-R0|)/2 Figure 3: Illustration of characterization of QRA complex

  9. Testing and Results RPR • Test using normal and abnormal data in MIT-PTB database and MIT-BIH arrhythmia database • Initially performed in the MATLAB platform • 30000 samples (Single lead data) • Resolution: 8-bit • sampling interval: 1 ms

  10. Testing and Results RPR • The evaluation criteria are Sensitivity (Re) and Positive Predictivity (P+), defined as: and TP (True-Positive): correctly found R peaks FN(False-Negative): missed R peaks FP: the number of misdetection

  11. Testing and Results RPR • MATLAB simulation • With mit-db, 60,000 samples • Re and P+ of 97.82 % • 98.35 % respectively • With ptb-db, 120 leads • An average sensitivity of 99.47 % • Predictivityof 95 %

  12. Testing and Results RPR • FPGA implementation • With a total of 100 single lead data, each containing 7,000 samples • An average Re and P+ of 94.8 % • 98.17 % respectively

  13. Conclusion RPR • The algorithm is implemented with synthetic ECG data from ptb-db and mit-db • In the present approach, 20 point average slope eliminates the effect of high frequency noise and to minimize the effect of any momentary spike • 2 point slope should not exceed the slope threshold criteria

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