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A new method for change-point detection developed for on-line analysis of the heart beat variability during sleep

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  • Staudacher, M.
  • Telser, S.
  • Amann, A.
  • Hinterhuber, H.
  • Ritsch-Marte, M.

Abstract

We present a novel scaling-dependent measure for times series analysis, the progressive detrended fluctuation analysis (PDFA). Since this method progressively includes and analyzes all data points of the time series, it is suitable for on-line change-point detection: Sudden changes in the statistics of the data points, in the type of correlation or in the statistical variance, or both, are reliably indicated and localized in time. This is first shown for numerous artificially generated data sets of Gaussian random numbers. Also time series with various non-stationarities, such as non-polynomial trends and “spiking”, are included as examples. Although generally applicable, our method was specifically developed as a tool for numerical sleep evaluation based on heart rate variability in the ECG-channel of polysomnographic whole night recordings. It is demonstrated that PDFA can detect specific sleep stage transitions, typically ascending transitions involving sympathetic activation as for example short episodes of wakefulness, and that the method is capable to discern between NREM sleep and REM sleep.

Suggested Citation

  • Staudacher, M. & Telser, S. & Amann, A. & Hinterhuber, H. & Ritsch-Marte, M., 2005. "A new method for change-point detection developed for on-line analysis of the heart beat variability during sleep," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 349(3), pages 582-596.
    • Handle: RePEc:eee:phsmap:v:349:y:2005:i:3:p:582-596
    DOI: 10.1016/j.physa.2004.10.026
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    References listed on IDEAS

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    1. Stanley, H.E. & Buldyrev, S.V. & Goldberger, A.L. & Goldberger, Z.D. & Havlin, S. & Mantegna, R.N. & Ossadnik, S.M. & Peng, C.-K. & Simons, M., 1994. "Statistical mechanics in biology: how ubiquitous are long-range correlations?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 205(1), pages 214-253.
    2. Linda E. Moody & Lois Lowry & Hossein Yarandi & Audrey Voss, 1997. "Psychophysiologic Predictors of Weaning from Mechanical Ventilation in Chronic Bronchitis and Emphysema," Clinical Nursing Research, , vol. 6(4), pages 311-330, November.
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    1. Jean-Marc Bardet & Imen Kammoun & Veronique Billat, 2012. "A new process for modeling heartbeat signals during exhaustive run with an adaptive estimator of its fractal parameters," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1331-1351, December.
    2. Hartmann, András & Mukli, Péter & Nagy, Zoltán & Kocsis, László & Hermán, Péter & Eke, András, 2013. "Real-time fractal signal processing in the time domain," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 89-102.
    3. Gulich, Damián & Zunino, Luciano, 2014. "A criterion for the determination of optimal scaling ranges in DFA and MF-DFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 397(C), pages 17-30.
    4. Pyko, Nikita S. & Pyko, Svetlana A. & Markelov, Oleg A. & Karimov, Artur I. & Butusov, Denis N. & Zolotukhin, Yaroslav V. & Uljanitski, Yuri D. & Bogachev, Mikhail I., 2018. "Assessment of cooperativity in complex systems with non-periodical dynamics: Comparison of five mutual information metrics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1054-1072.
    5. Del Pin, Enrico & Carniel, Roberto & Tárraga, Marta, 2008. "Event recognition by detrended fluctuation analysis: An application to Teide–Pico Viejo volcanic complex, Tenerife, Spain," Chaos, Solitons & Fractals, Elsevier, vol. 36(5), pages 1173-1180.
    6. Shu, Lei & Chen, Yu & Zhang, Weiping & Wang, Xueqin, 2022. "Spatial rank-based high-dimensional change point detection via random integration," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    7. Neil Hwang & Jiarui Xu & Shirshendu Chatterjee & Sharmodeep Bhattacharyya, 2022. "The Bethe Hessian and Information Theoretic Approaches for Online Change-Point Detection in Network Data," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 283-320, June.
    8. Jeske, Daniel R. & Montes De Oca, Veronica & Bischoff, Wolfgang & Marvasti, Mazda, 2009. "Cusum techniques for timeslot sequences with applications to network surveillance," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4332-4344, October.

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