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Annual Review of Statistics and Its Application

Volume 4, 2017

Online Analysis of Medical Time Series

Abstract

Complex, often high-dimensional time series are observed in medical applications such as intensive care. We review statistical tools for intelligent alarm systems, which are helpful for guiding medical decision-making in time-critical situations. The procedures described can also be applied for decision support or in closed-loop controllers. Robust time series filters allow one to extract a signal in the form of a time-varying trend with little or no delay. Additional rules—based, for instance, on suitably designed statistical tests—can be incorporated to preserve or detect interesting patterns such as level shifts or trend changes. Statistical pattern detection is a useful preprocessing step for decision-support systems. Dimension reduction techniques allow the compression of the often high-dimensional time series into a few variables containing most of the information inherent in the observed data. Combining such techniques with tools for analyzing the relationships among the variables in the form of large partial correlations or similar trend behavior improves the interpretability of the extracted variables and provides information that is thus meaningful to physicians.

Keyword(s): change-point detection, dimension reduction, online monitoring, robust filtering
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/content/journals/10.1146/annurev-statistics-060116-054148
2017-03-07
2024-04-19
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Literature Cited

  1. Abbas S, Fried R. 2016. Control charts for the mean based on robust two-sample tests SFB 823 Discuss. Pap. 21/16, TU Dortmund Univ., Dortmund, Ger.
  2. Abelson RP, Tukey JW. 1963. Efficient utilization of non-numerical information in quantitative analysis: general theory and the case of simple order. Ann. Math. Stat. 34:1347–69 [Google Scholar]
  3. Astola J, Heinonen P, Neuvo Y. 1989. Linear median hybrid filters. IEEE Trans. Circuits Syst. 36:1430–38 [Google Scholar]
  4. Bernholt T, Fried R. 2003. Computing the update of the repeated median regression line in linear time. Inf. Process. Lett. 88:111–17 [Google Scholar]
  5. Bernholt T, Fried R, Gather U, Wegener I. 2006. Modified repeated median filters. Stat. Comput. 16:177–92 [Google Scholar]
  6. Borowski M, Busse D, Fried R. 2015. Robust online-surveillance of trend-coherence in multivariate data streams. Stat. Comput. 25:913–28 [Google Scholar]
  7. Borowski M, Fried R. 2014. Online signal extraction by robust regression in moving windows with data-adaptive width selection. Stat. Comput. 24:597–613 [Google Scholar]
  8. Borowski M, Görges M, Fried R, Such O, Wrede C, Imhoff M. 2011a. VDE Position Paper: Medical device alarms. Biomed. Technik 56:73–83 [Google Scholar]
  9. Borowski M, Rudak N, Hussong B, Wied D, Kuhnt S, Tillmann W. 2014. On- and offline detection of structural breaks in thermal spraying processes. J. Appl. Stat. 41:1073–90 [Google Scholar]
  10. Borowski M, Schettlinger K, Gather U. 2009. Multivariate real time signal processing by a robust adaptive regression filter. Commun. Stat. Simulat. 38:426–40 [Google Scholar]
  11. Borowski M, Siebig S, Wrede C, Imhoff M. 2011b. Reducing false alarms of intensive care online-monitoring systems: an evaluation of two signal extraction algorithms. Comput. Math. Methods Med. 2011:143480 [Google Scholar]
  12. Box GEP, Jenkins GM, Reinsel GC. 2008. Time Series Analysis: Forecasting and Control New York: Wiley, 4th ed..
  13. Brillinger DR. 1981. Time Series: Data Analysis and Theory San Francisco: Holden-Day
  14. Brillinger DR. 1996. Remarks concerning graphical models for time series and point processes. Rev. Econom. 16:1–23 [Google Scholar]
  15. Brockwell PJ, Davis RA. 2010. Introduction to Time Series and Forecasting New York: Springer, 2nd ed..
  16. Calvet LE, Czellar V, Ronchetti E. 2015. Robust filtering. J. Am. Stat. Assoc. 110:1591–606 [Google Scholar]
  17. Charbonnier S. 2005. On-line extraction of temporal episodes from ICU high-frequency data: a visual support for signal interpretation. Comput. Methods Programs Biomed. 78:115–32 [Google Scholar]
  18. Charbonnier S, Becq G, Biot L. 2004. On-line segmentation algorithm for continuously monitored data in intensive care units. IEEE Trans. Biomed. Eng. 51:484–92 [Google Scholar]
  19. Charbonnier S, Garcia-Beltan C, Cadet C, Gentil S. 2005. Trend extraction and analysis for complex system monitoring and decision support. Eng. Appl. Artif. Intel. 18:21–36 [Google Scholar]
  20. Charbonnier S, Gentil S. 2010. On-line adaptive trend extraction of multiple physiological signals for alarm filtering in intensive care units. Int. J. Adapt. Control 24:382–408 [Google Scholar]
  21. Dahlhaus R. 2000. Graphical interaction models for multivariate time series. Metrika 51:157–72 [Google Scholar]
  22. Daumer M, Falk M. 1998. On-line change-point detection (for state space models) using multi-process Kalman filters. Linear Algebra Appl. 284:125–35 [Google Scholar]
  23. Davies PL, Fried R, Gather U. 2004. Robust signal extraction for on-line monitoring data. J. Stat. Plann. Inf. 122:65–78 [Google Scholar]
  24. Davies PL, Gather U. 2005. Breakdown and groups. Ann. Stat. 33:977–1035 [Google Scholar]
  25. Forni M, Hallin M, Lippi M, Reichlin L. 2005. The generalized dynamic factor model. J. Am. Stat. Assoc. 100:830–40 [Google Scholar]
  26. Fried R. 2004. Robust filtering of time series with trends. Nonparametric Stat. 16:313–28 [Google Scholar]
  27. Fried R. 2007. On the robust detection of edges in time series filtering. Comput. Stat. Data Anal. 52:1063–74 [Google Scholar]
  28. Fried R. 2012. On the online estimation of piecewise constant volatilities. Comput. Stat. Data Anal. 56:3080–90 [Google Scholar]
  29. Fried R, Bernholt T, Gather U. 2006. Repeated median and hybrid filters. Comput. Stat. Data Anal. 50:2313–38 [Google Scholar]
  30. Fried R, Dehling H. 2011. Robust nonparametric tests for the two sample location problem. Stat. Method Appl. 20:409–22 [Google Scholar]
  31. Fried R, Einbeck J, Gather U. 2007. Weighted repeated median smoothing and filtering. J. Am. Stat. Assoc. 102:1300–8 [Google Scholar]
  32. Fried R, Gather U. 2007. On rank tests for shift detection in time series. Comput. Stat. Data Anal. 52:221–33 [Google Scholar]
  33. Fried R, Imhoff M. 2004. On the online detection of monotonic trends in time series. Biom. J. 46:90–102 [Google Scholar]
  34. Fried R, Schettlinger K, Borowski M. 2012. robfilter: robust time series filters. R package with set of functions to filter time series based on concepts from robust statistics. https://cran.r-project.org/web/packages/robfilter/index.html
  35. Gather U, Bauer M, Fried R. 2002a. The identification of multiple outliers in online monitoring data. Estadística 54:289–338 [Google Scholar]
  36. Gather U, Fried R. 2003. Robust estimation of scale for local linear temporal trends. Tatra Mt. Math. Publ. 26:87–101 [Google Scholar]
  37. Gather U, Fried R. 2004. Methods and algorithms for robust filtering. COMPSTAT 2004—Comput. Stat. 16th Symp., Prague J Antoch 159–70 Heidelberg: Physika [Google Scholar]
  38. Gather U, Fried R, Imhoff M. 2000. Online classification of states in intensive care. Data Analysis W Gaul, O Opitz, M Schader 413–28 Berlin: Springer [Google Scholar]
  39. Gather U, Fried R, Imhoff M, Becker C. 2002b. Patterns of dependencies in dynamic multivariate data. ESF Explor. Workshop Pattern Detect. Discov. Data Mining: Lect. Notes Comput. Sci. 2447 D Hand, N Adams 214–26 Berlin: Springer [Google Scholar]
  40. Gather U, Fried R, Lanius V, Imhoff M. 2001. Online monitoring of high dimensional physiological time series—a case study. Estadistica 53:259–98 [Google Scholar]
  41. Gather U, Imhoff M, Fried R. 2002c. Graphical models for multivariate time series from intensive care monitoring. Stat. Med. 21:2685–701 [Google Scholar]
  42. Gather U, Schettlinger K, Fried R. 2006. Online signal extraction by robust linear regression. Comput. Stat. 21:33–51 [Google Scholar]
  43. Gelper S, Schettlinger K, Croux C, Gather U. 2009. Robust online scale estimation in time series: a model-free approach. J. Stat. Plan. Inference 139:335–49 [Google Scholar]
  44. Gnanadesikan R, Kettenring JR. 1972. Robust estimates, residuals and outlier detection with multiresponse data. Biometrics 28:81–124 [Google Scholar]
  45. Gordon K, Smith AFM. 1990. Modeling and monitoring biomedical time series. J. Am. Stat. Assoc. 85:328–37 [Google Scholar]
  46. Görges M, Markewitz BA, Westenskow DR. 2009. Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth. Analg. 108:1546–52 [Google Scholar]
  47. Haimowitz IJ, Le PP, Kohane IS. 1995. Clinical monitoring using regression-based trend templates. Artif. Intell. Med. 6:473–96 [Google Scholar]
  48. Hampel FR. 1975. Beyond location parameters: robust concepts and methods. Proc. 40th Sess. Int. Stat. Inst. 46:375–82 [Google Scholar]
  49. Heinonen P, Neuvo Y. 1987. FIR-median hybrid filters. IEEE Trans. Acoust. Speech 35:832–38 [Google Scholar]
  50. Heinonen P, Neuvo Y. 1988. FIR-median hybrid filters with predictive FIR substructures. IEEE Trans. Acoust. Speech 36:892–99 [Google Scholar]
  51. Hill DW, Emdresen J. 1978. Trend recording and forecasting in intensive care therapy. Br. J. Clin. Equip. 1:5–14 [Google Scholar]
  52. Hodges JL, Lehmann EL. 1963. Estimates of location based on rank tests. Ann. Math. Stat. 34:598–611 [Google Scholar]
  53. IEC, ISO (Int. Electrotech. Comm. Int., Organ. Stand.) 2001. Medical electrical equipment—part 2-12: particular requirements for basic safety and essential performance of critical care ventilators. ISO Standards Catalogue ISO/TC 121—Anaesthetic and Respiratory Equipment. Geneva: ISO [Google Scholar]
  54. IEC, ISO (Int. Electrotech. Comm., Int. Organ. Stand.) 2006. Medical electrical equipment—part 1-8: general requirements for basic safety and essential performance—collateral standard: general requirements, tests and guidance for alarm systems in medical electrical equipment and medical electrical systems. ISO Standards Catalogue ISO/TC 121—Anaesthetic and Respiratory Equipment. Geneva: ISO [Google Scholar]
  55. IEC, ISO (Int. Electrotech. Comm. Int., Organ. Stand.) 2007. Medical electrical equipment – Part 1-10: General requirements for basic safety and essential performance – Collateral standard: Requirements for the development of physiologic closed-loop controllers. ISO Standards Catalogue ISO/TC 121—Anaesthetic and Respiratory Equipment. Geneva: ISO [Google Scholar]
  56. Imhoff M, Bauer M, Gather U, Fried R. 2002. Pattern detection in intensive care monitoring time series with autoregressive models: influence of the model order. Biometrical J. 44:746–61 [Google Scholar]
  57. Imhoff M, Bauer M, Gather U, Löhlein D. 1998. Statistical pattern detection in univariate time series of intensive care on-line monitoring data. Intensive Care Med. 24:1305–14 [Google Scholar]
  58. Imhoff M, Fried R. 2009. The crying wolf: still crying?. Anesth. Analg. 108:1382–83 [Google Scholar]
  59. Imhoff M, Gather U, Morik K. 1999. Development of decision support algorithms for intensive care medicine: a new approach combining time series analysis and a knowledge base system with learning and revision capabilities. KI-99: Advances in Artificial Intelligence W Brugard, T Christaller, AB Cremers 219–30 Lect. Notes Comput. Sci 1701 Berlin: Springer [Google Scholar]
  60. Kennedy RR. 1995. A modified Trigg's tracking variable as an `advisory’ alarm during anaesthesia. Int. J. Clin. Monit. Comput. 12:197–204 [Google Scholar]
  61. Lanius V, Gather U. 2010. Robust online signal extraction from multivariate time series. Comput. Stat. Data Anal. 54:966–75 [Google Scholar]
  62. Lee Y, Kassam S. 1985. Generalized median filtering and related nonlinear filtering techniques. IEEE Trans. Acoust. Speech 33:672–83 [Google Scholar]
  63. Li WL, Sun SH, Jia YM, Du JP. 2016. Robust unscented Kalman filter with adaptation of process and measurement noise covariances. Digit. Signal Process. 48:93–103 [Google Scholar]
  64. Mäkivirta A, Koski E, Kari A, Sukuvaara T. 1991. The median filter as a preprocessor for a patient monitor limit alarm system in intensive care. Comput. Methods Programs Biomed. 34:139–44 [Google Scholar]
  65. Morell O, Fried R. 2008. On nonparametric tests for trend detection in time series. Statistical Inference, Econometric Analysis and Matrix Algebra: Festschrift in Honour of Götz Trenkler B Schipp, W Krämer 19–40 Heidelberg: Springer [Google Scholar]
  66. Morris A. 1998. Algorithm-based decision-making. Principles and Practice of Intensive Care Monitoring JA Tobin 1355–81 New York: McGraw-Hill [Google Scholar]
  67. Müller CH. 1999. On the use of high breakdown point estimators in the image analysis. Tatra Mt. Math. Publ. 17:283–93 [Google Scholar]
  68. Nunkesser R, Fried R, Schettlinger K, Gather U. 2009. Robust time series analysis by the Qn-estimator. Comput. Stat. Data Anal. 53:2354–62 [Google Scholar]
  69. Peña D, Box GEP. 1987. Identifying a simplifying structure in time series. J. Am. Stat. Assoc. 82:836–43 [Google Scholar]
  70. Peña D, Yohai VJ. 2015. Generalized dynamic principal components. J. Am. Stat. Assoc. 1111121–31
  71. Rousseeuw PJ. 1984. Least median of squares regression. J. Am. Stat. Assoc. 79:871–80 [Google Scholar]
  72. Rousseeuw PJ, Croux C. 1993. Alternatives to the median absolute deviation. J. Am. Stat. Assoc. 88:1273–83 [Google Scholar]
  73. Rousseeuw PJ, Hubert M. 1999. Regression depth. J. Am. Stat. Assoc. 94:388–402 [Google Scholar]
  74. Rousseeuw PJ, Leroy AM. 1987. Robust Regression and Outlier Detection New York: Wiley
  75. Ruckdeschel P, Spangl B. 2009. robKalman: robust Kalman filtering. R package for statistical data analysis. https://r-forge.r-project.org/R/?group_id=88
  76. Ruckdeschel P, Spangl B, Pupashenko D. 2016. Robust Kalman tracking and smoothing with propagating and non-propagating outliers. arXiv1204.3358 [math.ST]
  77. Schettlinger K, Fried R, Gather U. 2010. Real time signal processing by adaptive repeated median filters. Int. J. Adapt. Control 24:346–62 [Google Scholar]
  78. Siebig S, Kuhls S, Imhoff M, Gather U, Scholmerich J, Wrede C. 2010. Intensive care unit alarms—how many do we need?. Crit. Care Med. 38:451–56 [Google Scholar]
  79. Siegel AF. 1982. Robust regression using repeated medians. Biometrika 69:242–44 [Google Scholar]
  80. Smith AFM, West M, Gordon K, Knapp MS, Trimble IMG. 1983. Monitoring kidney transplant patients. Statistician 32:46–54 [Google Scholar]
  81. Trimble IM, West M, Knapp MS, Pownall R, Smith AF. 1983. Detection of renal allograft rejection by computer. BMJ 286:1695–99 [Google Scholar]
  82. Tukey JW. 1977. Exploratory Data Analysis Reading, MA: Addison-Wesley
  83. Wichman R, Astola JT, Heinonen PJ, Neuvo YA. 1990. FIR-median hybrid filters with excellent transient response in noisy conditions. IEEE Trans. Acoust. Speech 38:2108–17 [Google Scholar]
  84. Williams CKI, Quinn JA, McIntosh N. 2006. Factorial switching Kalman filters for condition monitoring in neonatal intensive care. Adv. Neural Inf. Process. Syst. 18:1513–20 [Google Scholar]
  85. Yang P, Dumont G, Ansermino JM. 2006. Adaptive change detection in heart rate trend monitoring in anesthetized children. IEEE Trans. Biomed. Eng. 53:2211–19 [Google Scholar]
  86. Yang P, Dumont GA, Ansermino JM. 2010. Online pattern recognition based on a generalized hidden Markov model for intraoperative vital sign monitoring. Int. J. Adapt. Control 24:363–81 [Google Scholar]
  87. Zou H, Hastie T, Tibshirani R. 2006. Sparse principle component analysis. J. Comput. Graph. Stat. 15:265–86 [Google Scholar]
/content/journals/10.1146/annurev-statistics-060116-054148
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Online Analysis of Medical Time Series
Annual Review of Statistics and Its Application 4, 169 (2017); https://doi.org/10.1146/annurev-statistics-060116-054148
/content/journals/10.1146/annurev-statistics-060116-054148
/content/journals/10.1146/annurev-statistics-060116-054148
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