A quality-on-demand algorithm for wavelet-based compression of electrocardiogram signals

• Published in: IEEE transactions on biomedical engineering Vol. 49; no. 3; pp. 233 - 239
• Main Authors: MIAOU, Shaou-Gang, LIN, Chih-Lung
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological and medical sciences; Biomedical imaging; Bit rate; Communications technology; Database systems; Electrocardiography; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; Humans; Image coding; Image reconstruction; Investigative techniques, diagnostic techniques (general aspects); Medical diagnostic imaging; Medical sciences; Signal design; Signal encoding; Signal processing algorithms; Signal Processing, Computer-Assisted; Signal reconstruction; Studies; Trees (mathematics); Wavelet transforms; Human; Evaluation; Data compression; Algorithm; Waiting time; Convergence; Electrodiagnosis; Wavelet transformation; Smoothing; Electrocardiography; Signal processing; Signal reconstruction; Root mean square value; Signal analysis
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/10.983457

For the compression of medical signals such as electrocardiogram (ECG), excellent reconstruction quality of a highly compressed signal can be obtained by using a wavelet-based approach. The most widely used objective quality criterion for the compressed ECG is called the percent of root-mean-square difference (PRD). In this paper, given a user-specified PRD, an algorithm is proposed to meet the PRD demand by searching for an appropriate bit rate in an automatic, smooth, and fast manner for the wavelet-based compression. The bit rate searching is modeled as a root-finding problem for a one-dimensional function, where an unknown rate-distortion curve represents the function and the desired rate is the root to be sought. A solution derived from root-finding methods in numerical analysis is proposed. The proposed solution is incorporated in a well-known wavelet-based coding strategy called set partitioning in hierarchical trees. ECG signals taken from the MIT/BIH database are tested, and excellent results in terms of convergence speed, quality variation, and coding performance are obtained.