title: A new hybrid algorithm for ECG signal compression based on the wavelet transformation of the linearly predicted error.
abstract: This paper describes a hybrid technique based on the combination of wavelet transform and linear prediction to achieve very effective electrocardiogram (ECG) data compression. First, the ECG signal is wavelet transformed using four different discrete wavelet transforms (Daubechies, Coiflet, Biorthogonal and Symmlet). All the wavelet transforms are based on dyadic scales and decompose the ECG signals into five detailed levels and one approximation. Then, the wavelet coefficients are linearly predicted, where the error corresponding to the difference between these coefficients and the predicted ones is minimized in order to get the best predictor. In particular, the residuals of the wavelet coefficients are uncorrelated and hence can be represented with fewer bits compared to the original signal. To further increase the compression rate, the residual sequence obtained after linear prediction is coded using a newly developed coding technique. As a result, a compression ratio (Cr) of 20 to 1 is achieved with percentage root-mean square difference (PRD) less than 4%. The algorithm is compared to an alternative compression algorithm based on the direct use of wavelet transforms. Experiments on selected records from the MIT-BIH arrhythmia database reveal that the proposed method is significantly more efficient in compression. The proposed compression scheme may find applications in digital Holter recording, in ECG signal archiving and in ECG data transmission through communication channels.
authors: Computer Science Department, Faculty of Sciences, Zarka Private University, Zarka, Jordan.
authors: S M
authors: SM
authors: Ahmeda
authors: M
authors: M
authors: Abo-Zahhad
mesh_terms: D000465:Algorithms; D001698:Biomedical Engineering; D003627:Data Interpretation, Statistical; D004562:Electrocardiography / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D008955:Models, Cardiovascular; D012815:Signal Processing, Computer-Assisted