Analysis of ECG data, for data compression.

Abstract

A number of papers on the subject of data reduction techniques applied to ECG Data have recently been published; however, the authors found that most of these articles did not consider quantization techniques, which can be effectively applied to ECG data without any complex parameter extraction procedures. In this paper the authors have looked at the effects of quantization on ECG data and techniques of reducing the amount of data needed to represent these signals. Basically, 3 data reduction techniques, linear prediction using differential pulse code modulation, spectral analysis and slope change detection are investigated and a relative assessment of their performance is presented. This analysis revealed that a slope change detection, as applied to prefiltered data, can be used to represent ECG data at a rate of 2 bits/sample, while maintaining the mean squared error and peak error below 1% and 5% respectively. This technique therefore gives an effective 3 to 1 reduction over the original sampled data, since it was found that the original data could be quantized to 6 bits without significant loss of waveform information.