ABSTRACT
A dual-tree sparse decomposition of discrete wavelet transforms technique is presented for the electrocardiogram (ECG) signals using a variable-length Huffman coding technique. In this method, a one-dimensional ECG signal is decomposed with as symmetry tree structure at each level using discrete wavelet transforms which outcomes from a larger quantity of insignificant coefficients. They are measured as zero amplitude value and represented as sparse datasets that improve the compression rate and Huffman coding helps to represent the signal with low bit rate data. These results compressed data codes of large ECG time-series datasets of the signal. Here, different wavelet filters evaluated for compression based on sparse data from wavelet decomposition. The performance of an algorithm in the term of compression 43.52% with a 99.9% correlation between original and recovered signals from compressed ECG data. Further, heart rate variability (HRV) analysis with correlation of R-R intervals in between the original and reconstructed ECG signal; it validates the reconstruction as well as sensitivity of compression technique towards data accuracy.
