On the improved correlative prediction scheme for aliased electrocardiogram (ECG) data compression.
An improved scheme for aliased electrocardiogram (ECG) data compression has been constructed, where the predictor exploits the correlative characteristics of adjacent QRS waveforms. The twin-R correlation prediction and lifting wavelet transform (LWT) for periodical ECG waves exhibits feasibility and high efficiency to achieve lower distortion rates with realizable compression ratio (CR); grey predictions via GM(1, 1) model have been adopted to evaluate the parametric performance for ECG data compression. Simulation results illuminate the validity of our approach.
Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ 85721, USA. xgao1985@email.arizona.edu
Xin
X
Gao
D004562:Electrocardiography / Q000379:methods*; D008962:Models, Theoretical; D011237:Predictive Value of Tests