Nonlinear Complexity Sorting Approach for 2D ECG Data Compression

Abstract

Cardiovascular Disease (CVD) is globally acknowledged research problem. The continuous Electrocardiogram (ECG) monitoring can assist in tackling the problem of CVD. The redundancy in the monitoring of ECG signal is reduced by various signal processing techniques either in 1D or 2D domain. This chapter is having the sole objective of reviewing the existing 2D ECG data compression techniques and comparing it with the 1D compression techniques. Furthermore, proposing a novel nonlinear complexity sorting approach for 2D ECG data compression. The broad basic steps involved in the procedure are preprocessing, transformation and encoding. The preprocessing steps includes QRS detection, 2D ECG image formulation, Dc quantization and complexity sorting. The second stage of transformation includes the various decomposition techniques. At encoding stage, standard image codec (JPEG2000) can be employed. The performance evaluation of the proposed complexity sorting algorithm is performed on records of Massachusetts Institute of Technology – Beth Israel Hospital arrhythmia database.