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 optimizing the quantization
scheme of wavelet-based ECG data compression based on a genetic algorithm (GA). The GA search can induce a
stationary relationship among the quantization scales of multi-resolution
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 quantization scheme
with linear distortion characteristic. The linear distortion property can be
almost independent of ECG signals and provide fast error control. The
compression performance and convergence speed of reconstruction quality
maintenance are also evaluated by using the MIT-BIH arrhythmia database.
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