ECG compression for remote healthcare systems using selective thresholding based on energy compaction

This paper presents a wavelet-based low-complexity Electrocardiogram (ECG) compression algorithm for mobile healthcare systems, in the backdrop of real clinical requirements. The proposed method aims at achieving good trade-off between the compression ratio (CR) and the fidelity of the reconstructed signal, to preserve the clinically diagnostic features. Keeping the computational complexity at a minimal level is paramount since the application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices. The proposed compression methodology is based on the Discrete Wavelet Transform (DWT). The energy packing efficiency of the DWT coefficients at different resolution levels is analysed and a thresholding policy is applied to select only those coefficients which have significant contribution to the original signal total energy. The proposed methodology is evaluated on normal and abnormal ECG signals extracted from the MIT-BIH database and achieves an average compression ratio of 16.5:1, an average percent root mean square difference of 0.75 and an average cross correlation value of 0.98.