A quality-on-demand electrocardiogram signal compression using modified set partitioning in hierarchical tree

Abstract

Biomedical signals enfold much crucial clinical information. Cardiac imperfection includes information on the morphology of its electrical signals. These signals are classically recorded over a considerable period, so the size of data file becomes bulky and hence compression is essential. This paper focuses on the implementation of electrocardiogram signal compression using wavelet-based progressive coding such as set partitioning in hierarchical tree and its modified version to achieve improvement in the speed at low bit rate. We obtained compression ratio up to 22:1 for MIT-BIH arrhythmia database record number 117 with a percent mean square difference of 0.9 and 0.73 % using orthogonal and biorthogonal wavelets, respectively. The coders accomplish bit rate control and produce a bit stream that is progressive in quality. It facilitates the user to trim the bit stream at desired point and make required quality restoration for the reduced file size with user-defined compression ratio or bit rate.

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