Improving storage efficiency of vector quantization codebook for physiological quasi-periodic signals

A quasi-periodic signal is a periodic signal with period and amplitude vari ations. The electrocardiogram (ECG) and several physiological signals can b e treated as quasi-periodic. Vector quantization (VQ) is a valuable and uni versal tool for signal compression. However, the periodicity of a quasi-per iodic signal causes data redundancy in the VQ codebook, where many codevect ors are highly correlated. This paper explores the codebook (CB) redundancy in order to increase storage efficiency for physiological quasi-periodic s ignals. A quantitative CB redundancy measure and two redundancy reducing al gorithms are proposed. Both algorithms use a mixed CB structure containing one and two-dimensional CBs. The first algorithm is applied to a CB directl y, and the second one uses an LBG-like training algorithm to obtain a stora ge-efficient CB from a set of training vectors. With the MIT/BIH ECG databa se, the experimental results show that both algorithms can reduce the CB re dundancy effectively with essentially no loss of signal quality. For compar ison, the mean-shape VQ (MSVQ) proposed by Cardenas-Barrera and Lorenzo-Gin ori for ECG compression is implemented and the resulting average percent of the root-mean-square difference (PRD) is 10.78%. By using the first algori thm, the CB storage space is reduced by 40% and the resulting average PRD i s 10.87%. The second algorithm can reduce the CB storage space by 75% and t he average PRD is 10.27%, which is even better than the original MSVQ. (C) 2001 IPEM. Published by Elsevier Science Ltd. All rights reserved.