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A novel recursive backtracking genetic programming-based algorithm for 12-lead ECG compression

  • Mohammad Feli
  • Fardin Abdali-MohammadiEmail author
Original Paper
  • 2 Downloads

Abstract

ECG signal is among medical signals used to diagnose heart problems. A large volume of medical signal’s data in telemedicine systems causes problems in storing and sending tasks. In the present paper, a recursive algorithm with backtracking approach is used for ECG signal compression. This recursive algorithm constructs a mathematical estimator function for each segment of the signal using genetic programming algorithm. When all estimator functions of different segments of the signal are determined and put together, a piecewise-defined function is constructed. This function is utilized to generate a reconstructed signal in the receiver. The compression result is a set of compressed strings representing the piecewise-defined function which is coded through a text compression method. In order to improve the compression results in this method, the input signal is smoothed. MIT-BIH arrhythmia database is employed to test and evaluate the proposed algorithm. The results of this algorithm include the average of compression ratio that equals 30.97 and the percent root-mean-square difference that is equal to 2.38%, suggesting its better efficiency in comparison with other state-of-the-art methods.

Keywords

Electrocardiograph Signal compression Genetic programming Backtracking algorithm 

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Engineering and Information TechnologyRazi UniversityKermanshahIran

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