Abstract
The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible to derive algorithms guaranteeing the smallest possible reconstruction error when a bounded selection of signal samples is interpolated. The proposed model resembles well-known network models and is solved by a cubic dynamic programming algorithm. When applied to standard test problems, the algorithm produces a compressed representation for which the distortion is about one-half of that obtained by traditional time-domain compression techniques at reasonable compression ratios. This illustrates that, in terms of the accuracy of decoded signals, existing time-domain heuristics for ECG compression may be far from what is theoretically achievable. The paper is an attempt to bridge this gap.
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References
Abernstein, J. P., andThompkins, W. J. (1982): ‘A new data reduction algorithm for real time ECG analysis’,IEEE Trans.,BME-29, pp. 43–48
Aneja, Y. P., andNair K. P. K. (1978): ‘The constrained shortest path problem’,Nav. Res. Log. Q.,25, pp. 549–553
Aneja, Y. P., Aggarwal, V., andNair, K. P. K. (1983): ‘Shortest chain subject to side conditions’,Networks,13, pp. 295–302
Barr, R. C. (1988): ‘Adaptive sampling of cardiac waveforms’,J. Electrocard.,21, pp. 57–60
Beasley, J. E., andChristofides, N. (1989): ‘An algorithm for the resource constrained shortest path problem’,Networks,19, pp. 379–394
Cox, J. R., Noile, F. M., Fozzard, H. A., andOliver, G. C. (1968): ‘AZTEC: pre-processing program for real-time ECG rhythm analysis’,IEEE Trans.,BME-15, pp. 128–129
Dijkstra, E. W. (1959): ‘A note on two problems in connection with graphs’,Numerische Math.,1, pp. 269–271
Handler, G. Y., andZang, I. (1980): ‘A dual algorithm for the constrained shortest path problem’,Networks,10, pp. 293–310
Heber, J. G., Haugland, D., andHusøy, J. H. (1996): ‘An efficient implementation of an optimal time domain ECG coder’.Proc. IEEE EMBS96, paper 5.4.3-4, Amsterdam, Holland
Jalaleddine, S. M. S., Hutchens, C. G., Strattan, R. D., andCoberly, W. A. (1990): ‘ECG data compression techniques—a unified approach’,IEEE Trans.,BME-37, (4), pp. 329–343
Joksch, H. C. (1966): ‘The shortest route problem with constraints’,J. Math. Anal. Appl.,14, pp. 191–197
Moody, G. (1992): ‘MIT-BIH arrhythmia database CD-ROM (2nd edn.), overview’Massachusetts Institute of Technology
Ribeiro, C., andMinoux, M. (1985): ‘A heuristic approach to hard constrained shortest path problems’,Discrete Appl. Math,10, pp. 125–137
Rosseel, M. (1968): ‘Comments on a paper by R. Saigal: A constrained shortest route problem’,Opns. Res.,16, pp. 1232–1234
Saigal, R. (1968): ‘A constrained shortest route problem’,Opns. Res.,16, pp. 205–209
Tai, S. C. (1991): ‘SLOPE—a real time ECG data compressor’,Med. Biol. Eng. Comput.,29, pp. 175–179
Tai, S. C. (1993): ‘AZTDIS—a two-phase real-time ECG data compressor’,J. Biomed. Eng.,15, pp. 510–515
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Haugland, D., Heber, J.G. & Husøy, J.H. Optimisation algorithms for ECG data compression. Med. Biol. Eng. Comput. 35, 420–424 (1997). https://doi.org/10.1007/BF02534101
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DOI: https://doi.org/10.1007/BF02534101