Open AccessECG compression with Douglas-Peucker algorithm and fractal interpolation
Author(s) -
Hichem Guedri,
Abdullah Bajahzar,
Hafedh Belmabrouk
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2021176
Subject(s) - fractal , algorithm , compression (physics) , fractal compression , interpolation (computer graphics) , data compression , iterated function system , compression ratio , ramer–douglas–peucker algorithm , computer science , iterated function , function (biology) , mathematics , artificial intelligence , image compression , image processing , image (mathematics) , physics , mathematical analysis , thermodynamics , evolutionary biology , biology , internal combustion engine , computation
In this paper, we propose a new ECG compression method using the fractal technique. The proposed approaches utilize the fact that ECG signals are a fractal curve. This algorithm consists of three steps: First, the original ECG signals are processed and they are converted into a 2-D array. Second, the Douglas-Peucker algorithm (DP) is used to detect critical points (compression phase). Finally, we used the fractal interpolation and the Iterated Function System (IFS) to generate missing points (decompression phase). The proposed (suggested) methodology is tested for different records selected from PhysioNet Database. The obtained results showed that the proposed method has various compression ratios and converges to a high value. The average compression ratios are between 3.19 and 27.58, and also, with a relatively low percentage error (PRD), if we compare it to other methods. Results depict also that the ECG signal can adequately retain its detailed structure when the PSNR exceeds 40 dB.