ECG compression using multilevel thresholding of wavelet coefficients

A wavelet-based electrocardiogram (ECG) compression algorithm is proposed in this paper. The proposed algorithm reduces the bit rate of ECG and preserves its main clinically diagnostic features intact by minimizing reconstructed signal distortion. The original signal is divided into blocks and each block goes through a discrete wavelet transform. A threshold based on energy packing efficiency of the wavelet coefficients is used to threshold the wavelet coefficients. To assess the proper applicability of the proposed technique we have evaluated the effect of threshold level selection on the quality of the reconstructed signal. To generalize the proposed method, the technique is tested for the compression of a large set of normal and abnormal ECG signals extracted from MIT-BIH database. The performance parameters of the compression algorithm are measured and an average compression ratio of 13.57 with percent root mean square difference (PRD) of 4.87 is achieved. This technique outperforms the recently published ones in most of the data sets of MIT-BIH database.