A rate distortion optimal ECG coding algorithm

• Published in: IEEE transactions on biomedical engineering Vol. 48; no. 1; pp. 28 - 40
• Main Authors: Nygaard, R., Melnikov, G., Katsaggelos, A.K.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.01.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Biological and medical sciences; Data Display; Electrocardiography; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; Frequency domain analysis; Graph theory; Heuristic algorithms; Heuristic methods; Inspection; Interpolation; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Methods; Optimization; Optimization algorithms; Rate-distortion; Signal distortion; Signal encoding; Signal processing; Signal processing algorithms; Signal Processing, Computer-Assisted; Studies; Testing; Time domain analysis; Upper bound; Human; Electrodiagnosis; Signal compression; Time domain method; Electrocardiography; Myocardium; Distortion; Circulatory system; Root mean square value; Implementation; Optimization
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/10.900246

Signal compression is an important problem encountered in many applications. Various techniques have been proposed over the years for addressing the problem. Here, the authors present a time domain algorithm based on the coding of line segments which are used to approximate the signal. These segments are fit in a way that is optimal in the rate distortion sense. Although the approach is applicable to any type of signal, the authors focus, in this paper, on the compression of electrocardiogram (ECG) signals. ECG signal compression has traditionally been tackled by heuristic approaches. However, it has been demonstrated (R. Nygaard and D. Haugland, Proc. Eur. Signal Processing Conf. (EUSIPCO), Island of Rhodes, Greece, p. 2473-6, 1998) that exact optimization algorithms outperform these heuristic approaches by a wide margin with respect to reconstruction error. By formulating the compression problem as a graph theory problem, known optimization theory can be applied in order to yield optimal compression. In this paper, the authors present an algorithm that will guarantee the smallest possible distortion among all methods applying linear interpolation given an upper bound on the available number of bits. Using a varied signal test set, extensive coding experiments are presented. The authors compare the results from their coding method to traditional time domain ECG compression methods, as well as, to more recently developed frequency domain methods. Evaluation is based both on percentage root-mean-square difference (PRD) performance measure and visual inspection of the reconstructed signals. The results demonstrate that the exact optimization methods have superior performance compared to both traditional ECG compression methods and the frequency domain methods.