Wavelet-based ECG data compression optimization with genetic algorithm

Copyright © 2013 Tsung-Ching Wu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. With a direct impact on compression performance, optimal quantization scheme is crucial for transform-based ECG data compression. However, traditional optimization schemes derived with signal adaption are commonly inherent with signal dependency and unsuitable for real-time application. In this paper, the variety of arrhythmia ECG signal is utilized for op-timizing the quantization scheme of wavelet-based ECG data compression based on a genetic algorithm (GA). The GA search can induce a stationary rela-tionship among the quantization scales of multi-res-olution levels. The stationary property facilitates the control of multi-level quantization scales with a single variable. For this aim, a three-dimensional (3-D) curve fitting technique is applied for deriving a quan-tization scheme with linear distortion characteristic. The linear distortion property can be almost inde-pendent of ECG signals and provide fast error con-trol. The compression performance and convergence speed of reconstruction quality maintenance are also evaluated by using the MIT-BIH arrhythmia data-base