Abstract
A new technique for ECG compression is presented. Each delineated ECG beat is period normalized by multirate processing and then amplitude normalized. The discrete wavelet transform (DWT), based on Daubechies-4 basis functions is applied on these normalized beats, after shifting each of them to the origin. The concatenation of the ordered DWT coefficients of these beats is a near-cyclostationary signal. An algorithm is proposed to select a set of common positions of the significant coefficients to be retained from each beat. Linear prediction is then applied to predict only these DWT coefficients of the current beat from the corresponding coefficients of a certain number of previous beats. Transmitting only the residuals of selected coefficients improves the compression. A significant advantage of this technique is that the maximum reconstruction error in any cycle does not occur in the diagnostically crucial QRS region, while achieving a compression of about 15:1 and a normalized root mean square error of about 10%.
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