New efficient fractal based compression method for electrocardiogram signals

In this paper a new efficient fractal based compression algorithm is proposed for electrocardiogram signals. The self-similarities in the ECG signals make them suitable to be compressed efficiently using fractal based methods. In the proposed method, as in the basic fractal based compression method, each part of the signal is mapped to another part with a reasonable error. The transformed maps are then stored instead of the original signal samples. The signal is built up using these transforms in an iterative process using an arbitrary initial signal. Here, the morphological information of ECG is incorporated to improve the compression algorithm in compression ratio, PRD and CC. As a novel point and in contrary to other methods there is no need to detect ECG complexes, this makes the algorithm more robust and accurate. The fixed size of blocks with rotated transformed blocks and optimal coefficients for the maximum similarity between blocks are employed. The proposed algorithm was tested on a reasonable set of MIT-BIH database signals. The experiments all showed that the proposed algorithm outperforms all reported fractal-based methods.