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Constrained by the symmetries of its Gaussian elements, traditional ECG dynamic model has difficulty in accurately representing complicated ECG waveforms. In order to overcome to this limitation, this paper proposes a generalized EDM by introducing asymmetric Gaussians into the model instead of symmetric ones. The generalized EDM is then applied to the model-based ECG denoising framework using an extended Kalman filter (EKF). Experiments are conducted based on the MIT-BIH Arrhythmia database, and the results show that the proposed EDM is able to model a wider range of ECG morphologies than the traditional one, and consequently improves the denoising performance.