This paper presents an efficient electrocardiogram (ECG) signals compression technique based on QRS detection, estimation, and\r\n2D DWT coefficients thresholding. Firstly, the original ECG signal is preprocessed by detecting QRS complex, then the difference\r\nbetween the preprocessed ECG signal and the estimated QRS-complex waveform is estimated. 2D approaches utilize the fact\r\nthat ECG signals generally show redundancy between adjacent beats and between adjacent samples. The error signal is cut and\r\naligned to form a 2-D matrix, then the 2-D matrix is wavelet transformed and the resulting wavelet coefficients are segmented\r\ninto groups and thresholded. There are two grouping techniques proposed to segment the DWT coefficients. The threshold level\r\nof each group of coefficients is calculated based on entropy of coefficients. The resulted thresholded DWT coefficients are coded\r\nusing the coding technique given in the work by (Abo-Zahhad and Rajoub, 2002). The compression algorithm is tested for 24\r\ndifferent records selected from the MIT-BIH Arrhythmia Database (MIT-BIH Arrhythmia Database). The experimental results\r\nshow that the proposed method achieves high compression ratio with relatively low distortion and low computational complexity\r\nin comparison with other methods.
Loading....