ECG compression using wavelet transform

Presents two novel algorithms for compression of multilead electrocardiograms (MLECGs). In the first, the MLECG was decorrelated using a discrete cosine transform (DCT) and then a discrete wavelet transform (DWT) was applied on each channel after the removal of the least significant channels in the DCT domain. Significant compression ratios (CR) were achieved by retaining only the consequential DWT coefficients. The full potential of DWT was further exploited by appropriate quantization of these coefficients. Near perfect reconstruction was obtained for a CR of 1:8. This approach is highly suited for real time applications including that of high frequency Holter ECG. In the other algorithm, first level of compression was achieved by truncating the DWT while storing the highly significant coefficients from the truncated portion. Transform domain pole-zero modeling (TDPM) was then applied on the DWT compressed ECG to achieve higher compression ratios. In TDPM the DCT of a cycle of ECG is modeled as the impulse response of a pole-zero filter using Smith's (1983) method. Further compression was achieved by the application of vector quantization (VQ) on the model parameters. This algorithm resulted in a CR of 1:80 without significant distortion. The accuracy of reconstruction was checked using both qualitative and quantitative measures. The proposed algorithms were validated on both standard and local database