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

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@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

Citations