An efficient coding algorithm for the compression of ECG signals using the wavelet transform

• Published in: IEEE transactions on biomedical engineering Vol. 49; no. 4; pp. 355 - 362
• Main Author: Rajoub, B.A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.04.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological and medical sciences; Continuous wavelet transforms; Data compression; Discrete wavelet transforms; Electrocardiography; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; Encoding; Humans; Image reconstruction; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Root mean square; Signal encoding; Signal processing; Signal Processing, Computer-Assisted; Signal reconstruction; Testing; Wavelet coefficients; Wavelet transforms; Human; Electrodiagnosis; Signal compression; Wavelet transformation; Coding; Electrocardiography; Signal processing; Algorithm; Comparative study; Optimization
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/10.991163

A wavelet-based electrocardiogram (ECG) data compression algorithm is proposed in this paper. The ECG signal is first preprocessed, the discrete wavelet transform (DWT) is then applied to the preprocessed signal. Preprocessing guarantees that the magnitudes of the wavelet coefficients be less than one, and reduces the reconstruction errors near both ends of the compressed signal. The DWT coefficients are divided into three groups, each group is thresholded using a threshold based on a desired energy packing efficiency. A binary significance map is then generated by scanning the wavelet decomposition coefficients and outputting a binary one if the scanned coefficient is significant, and a binary zero if it is-insignificant. Compression is achieved by 1) using a variable length code based on run length encoding to compress the significance map and 2) using direct binary representation for representing the significant coefficients. The ability of the coding algorithm to compress ECG signals is investigated, the results were obtained by compressing and decompressing the test signals. The proposed algorithm is compared with direct-based and wavelet-based compression algorithms and showed superior performance. A compression ratio of 24:1 was achieved for MIT-BIH record 117 with a percent root mean square difference as low as 1.08%.