ECG data compression using optimal non-orthogonal wavelet transform

This paper introduces an effective technique for the compression of electro cardiogram (ECG) signals. The technique is based on a new class of non-orth ogonal discrete wavelet transform (DWT). The performance of ECG compression algorithm is measured by its ability to minimize distortion while retainin g all clinically significant features of the signal. The percent root-mean square difference (PRD) is used as an accepted standard for measuring the s ignal distortion. However, there is no standard for measuring the clinicall y significant features retained after signal reconstruction. The coefficien ts of the DWT are calculated such that the square of the difference between the original signal and the reconstructed one is minimum in least mean squ are sense. The resulting transforms deal with signals of arbitrary lengths; that means the signal length is not restricted to be a multiple of power o f 2. Numerical results comparing the performance of the constructed non-ort hogonal transform with that of W-transform and Daubechies D-4 orthogonal tr ansform are given. These results show that, independent of signal length, t he decomposition of the signal up to the fourth level is sufficient for get ting minimum PRD. In addition, the proposed technique yields the lowest PRD compared to the Ether two algorithms and for a compression ratio less than 10 the optimal transform can be obtained for only one ECG period. However, for a higher compression ratio the PRD is smaller for long signals. (C) 20 00 TPEM. Published by Elsevier Science Ltd. All rights reserved.