Compression of ECG signals based on optimum quantization of discrete cosine transform coefficients and Golomb-Rice coding

Abstract

This paper proposes an ECG signal compressor based on optimum quantization of discrete cosine transform (DCT) coefficients and Golomb-Rice coding. The ECG to be compressed is initially partitioned in blocks, and each DCT block is quantized using a quantization step size vector and a zeroing threshold vector. These vectors are defined so that the estimated entropy is minimized for a target distortion in the reconstructed signal or, alternatively, the distortion is minimized for a target entropy. The final step of the compressor is based on Golomb-Rice coding. To assess the performance of the compressor, records of the MIT-BIH Arrhythmia Database were compressed at various distortion levels, measured by the percent root-mean-square difference (PRD), and compression ratios (CR) were computed. An average CR of 10.4:1 was achieved for PRD equal to 2.5%.