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Witha direct impact on compression performance, optimal quantization scheme iscrucial for transform-based ECG data compression. However, traditionaloptimization schemes derived with signal adaption are commonly inherent withsignal dependency and unsuitable for real-time application. In this paper, thevariety of arrhythmia ECG signal is utilized for optimizing the quantizationscheme of wavelet-based ECG data compression based on a genetic algorithm GA. The GA search can induce astationary relationship among the quantization scales of multi-resolutionlevels. The stationary property facilitates the control of multi-levelquantization scales with a single variable. For this aim, a three-dimensional3-D curve fitting technique is applied for deriving a quantization schemewith linear distortion characteristic. The linear distortion property can bealmost independent of ECG signals and provide fast error control. Thecompression performance and convergence speed of reconstruction qualitymaintenance are also evaluated by using the MIT-BIH arrhythmia database.


Electrocardiogram; Error Control; Quantization Scale

Cite this paper

Wu, T. , Hung, K. , Liu, J. and Liu, T. 2013 Wavelet-based ECG data compression optimization with genetic algorithm. Journal of Biomedical Science and Engineering, 6, 746-753. doi: 10.4236-jbise.2013.67092.

Autor: Tsung-Ching Wu, King-Chu Hung, Je-Hung Liu, Tung-Kuan Liu

Fuente: http://www.scirp.org/


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