A novel fractal-based ECG compression algorithm achieves high compression ratios while maintaining signal quality, potentially improving ECG data storage and transmission.
May aid ECG storage efficiency; leaves open clinical validation before any practice adoption.
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.
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Guedri et al. (2021) studied this question.
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