A New Compression Scheme Based on Adaptive Vector Quantization and Singular Value Decomposition

Abstract

This paper presents a new technique for electrocardiogram data compression. The proposed method is based on the vector quantization and the singular value decomposition. First, the singular value decomposition is performed on the two-dimensional representation of the electrocardiogram signal and then a limited number of the weighted right singular vectors is quantized using the vector quantization. In addition, a residual encoding based on the singular value decomposition of the residual error is proposed. Consequently, a low reconstruction error was obtained. A new scheme is also proposed for the generation of the vector quantization codebook, it is based on the Lindth-Buzo-Gray algorithm, but in the proposed technique, at each vector quantization stage, a new codebook is generated. This latter is only produced from the retained singular vectors. Several tests were conducted using arrhythmia database of the Massachusetts Institute of Technology-Beth Israel Hospital. The obtained results were very satisfactory. Unlike most existing methods, in the proposed scheme, the codebook storage was taken into account during the calculation of the compression ratio which was not affected, since high compression ratios were obtained. For instance, a compression ratio of 102.54 was obtained in the case of the percent root mean square difference of 1.02%.