Created by W.Langdon from gp-bibliography.bib Revision:1.7954

- @Article{feli:SIVP,
- author = "Mohammad Feli and Fardin Abdali-Mohammadi",
- title = "A novel recursive backtracking genetic programming-based algorithm for 12-lead {ECG} compression",
- journal = "Signal, Image and Video Processing",
- year = "2019",
- volume = "13",
- number = "5",
- pages = "1029--1036",
- month = jul,
- keywords = "genetic algorithms, genetic programming, Electrocardiograph, Signal compression, Backtracking algorithm",
- URL = "http://link.springer.com/article/10.1007/s11760-019-01441-4",
- DOI = "doi:10.1007/s11760-019-01441-4",
- size = "8 pages",
- 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 used 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 2.38percent, suggesting its better efficiency in comparison with other state-of-the-art methods.",
- }

Genetic Programming entries for Mohammad Feli Fardin Abdali-Mohammadi