Quality driven gold washing adaptive vector quantization and its application to ECG data compression.

Abstract

The gold washing (GW) adaptive vector quantization (AVQ) (GW-AVQ) is a relatively new scheme for data compression. The adaptive nature of the algorithm provides the robustness for wide variety of the signals. However, the performance of GW-AVQ is highly dependent on a preset parameter called distortion threshold (dth) which must be determined by experience or trial-and-error. We propose an algorithm that allows us to assign an initial dth arbitrarily and then automatically progress toward a desired dth according to a specified quality criterion, such as the percent of root mean square difference (PRD) for electrocardiogram (ECG) signals. A theoretical foundation of the algorithm is also presented. This algorithm is particularly useful when multiple GW-AVQ codebooks and, thus, multiple dth's are required in a subband coding framework. Four sets of ECG data with entirely different characteristics are selected from the MIT/BIH database to verify the proposed algorithm. Both the direct GW-AVQ and a wavelet-based GW-AVQ are tested. The results show that a user specified PRD can always be reached regardless of the ECG waveforms, the initial selection of dth or whether a wavelet transform is used in conjunction with the GW-AVQ. An average result of 6% in PRD and 410 bits/s in compressed data rate is obtained with excellent visual quality.