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Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-Term Data Using Limited Prior Knowledge

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Abstract

This paper describes novel fully automated techniques for analyzing large amounts of cardiovascular data. In contrast to traditional medical expert systems our techniques incorporate no a priori knowledge about disease states. This facilitates the discovery of unexpected events. We start by transforming continuous waveform signals into symbolic strings derived directly from the data. Morphological features are used to partition heart beats into clusters by maximizing the dynamic time-warped sequence-aligned separation of clusters. Each cluster is assigned a symbol, and the original signal is replaced by the corresponding sequence of symbols. The symbolization process allows us to shift from the analysis of raw signals to the analysis of sequences of symbols. This discrete representation reduces the amount of data by several orders of magnitude, making the search space for discovering interesting activity more manageable. We describe techniques that operate in this symbolic domain to discover rhythms, transient patterns, abnormal changes in entropy, and clinically significant relationships among multiple streams of physiological data. We tested our techniques on cardiologist-annotated ECG data from forty-eight patients. Our process for labeling heart beats produced results that were consistent with the cardiologist supplied labels 98.6 of the time, and often provided relevant finer-grained distinctions. Our higher level analysis techniques proved effective at identifying clinically relevant activity not only from symbolized ECG streams, but also from multimodal data obtained by symbolizing ECG and other physiological data streams. Using no prior knowledge, our analysis techniques uncovered examples of ventricular bigeminy and trigeminy, ectopic atrial rhythms with aberrant ventricular conduction, paroxysmal atrial tachyarrhythmias, atrial fibrillation, and pulsus paradoxus.

References

  1. 1.

    Kopec D, Kabir MH, Reinharth D, Rothschild O, Castiglione JA: Human errors in medical practice: systematic classification and reduction with automated information systems. Journal of Medical Systems 2003,27(4):297-313. 10.1023/A:1023796918654

  2. 2.

    Martich GD, Waldmann CS, Imhoff M: Clinical informatics in critical care. Journal of Intensive Care Medicine 2004,19(3):154-163. 10.1177/0885066604264016

  3. 3.

    Syed Z, Guttag J: Prototypical biological signals. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '07), April 2007, Honolulu, Hawaii, U.S.A.

  4. 4.

    Daw CS, Finney CEA, Tracy ER: A review of symbolic analysis of experimental data. Review of Scientific Instruments 2003,74(2):915-930. 10.1063/1.1531823

  5. 5.

    Braunwald E, Zipes D, Libby P: Heart Disease: A Textbook of Cardiovascular Medicine. WB Saunders, Philadelphia, Pa, USA; 2001.

  6. 6.

    Cuesta-Frau D, Pérez-Cortés JC, Andreu-García G: Clustering of electrocardiograph signals in computer-aided Holter analysis. Computer Methods and Programs in Biomedicine 2003,72(3):179-196. 10.1016/S0169-2607(02)00145-1

  7. 7.

    Myers CS, Rabiner LR: A comparative study of several dynamic time-warping algorithms for connected-word recognition. The Bell System Technical Journal 1981,60(7):1389-1409.

  8. 8.

    Donoho DL: De-noising by soft-thresholding. IEEE Transactions on Information Theory 1995,41(3):613-627. 10.1109/18.382009

  9. 9.

    Chen G, Wei Q, Zhang H: Discovering similar time-series patterns with fuzzy clustering and DTW methods. Proceedings of Joint 9th IFSA World Congress and 20th NAFIPS International Conference (NAFIPS '01), July 2001, Vancouver, BC, Canada 4: 2160–2164.

  10. 10.

    Keogh EJ, Pazzani MJ: Scaling up dynamic time warping for data mining applications. Proceeding of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '00), August 2000, Boston, Mass, USA 285–289.

  11. 11.

    Gonzalez TF: Clustering to minimize the maximum intercluster distance. Theoretical Computer Science 1985,38(2-3):293-306.

  12. 12.

    Fraden J, Neuman MR: QRS wave detection. Medical and Biological Engineering and Computing 1980,18(2):125-132. 10.1007/BF02443287

  13. 13.

    Hamming R: Error-detecting and error-checking codes. The Bell System Technical Journal 1950,29(2):147-160.

  14. 14.

    Landau GM, Schmidt JP, Sokol D: An algorithm for approximate tandem repeats. Journal of Computational Biology 2001,8(1):1-18. 10.1089/106652701300099038

  15. 15.

    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ: Basic local alignment search tool. Journal of Molecular Biology 1990,215(3):403-410.

  16. 16.

    Jennings D, Amabile T, Ross L: Informal covariation assessments: data-based versus theory-based judgements. In Judgement Under Uncertainty: Heuristics and Biases. Cambridge University Press, Cambridge, UK; 1982:211-230.

  17. 17.

    Baumert M, Baier V, Truebner S, Schirdewan A, Voss A: Short- and long-term joint symbolic dynamics of heart rate and blood pressure in dilated cardiomyopathy. IEEE Transactions on Biomedical Engineering 2005,52(12):2112-2115. 10.1109/TBME.2005.857636

  18. 18.

    Abramson N: Information Theory and Coding. McGraw Hill, New York, NY, USA; 1963.

  19. 19.

    Kojadinovic I:Relevance measures for subset variable selection in regression problems based on-additive mutual information. Computational Statistics & Data Analysis 2005,49(4):1205-1227. 10.1016/j.csda.2004.07.026

  20. 20.

    Holter NJ: New method for heart studies. Science 1961,134(3486):1214-1220. 10.1126/science.134.3486.1214

  21. 21.

    Agarwal R, Gotman J, Flanagan D, Rosenblatt B: Automatic EEG analysis during long-term monitoring in the ICU. Electroencephalography and Clinical Neurophysiology 1998,107(1):44-58. 10.1016/S0013-4694(98)00009-1

  22. 22.

    Lagerholm M, Peterson C, Braccini G, Edenbrandt L, Sörnmo L: Clustering ECG complexes using hermite functions and self-organizing maps. IEEE Transactions on Biomedical Engineering 2000,47(7):838-848. 10.1109/10.846677

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Correspondence to Zeeshan Syed.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://doi.org/creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Syed, Z., Guttag, J. & Stultz, C. Clustering and Symbolic Analysis of Cardiovascular Signals: Discovery and Visualization of Medically Relevant Patterns in Long-Term Data Using Limited Prior Knowledge. EURASIP J. Adv. Signal Process. 2007, 067938 (2007) doi:10.1155/2007/67938

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Keywords

  • Atrial Tachyarrhythmia
  • Symbolic Analysis
  • Ventricular Conduction
  • Multimodal Data
  • High Level Analysis