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

Staudacher, Martin; Telser, Stefan; Amann, Anton; Hinterhuber, Hartmann; Ritsch‐Marte, Monika

doi:10.1016/j.physa.2004.10.026

Cited by 74 publications

(37 citation statements)

References 24 publications

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“…Finally, a continuous DFA method has been suggested in the context of studying heartbeat data during sleep [39,40]. The method compares unnormalized fluctuation functions F (s) for increasing length of the data.…”

Section: Further Modifications and Extensions Of Dfamentioning

confidence: 99%

“…• the Modified Detrended Fluctuation Analysis (MDFA) [38], which is essentially a mixture of old FA and DFA, • the continuous DFA (CDFA) technique [39,40], which is particularly intended for the detection of transitions, • the Fourier DFA [41], • a variant of DFA based on empirical mode decomposition (EMD) [42], • a variant of DFA based on singular value decomposition (SVD) [43,44], and • a variant of DFA based on high-pass filtering [45].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of detrending methods for fluctuation analysis

Bashan

Bartsch

Kantelhardt

et al. 2008

Physica A: Statistical Mechanics and its Applications

290

187

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We examine several recently suggested methods for the detection of long-range correlations in data series based on similar ideas as the well-established Detrended Fluctuation Analysis (DFA). In particular, we present a detailed comparison between the regular DFA and two recently suggested methods: the Centered Moving Average (CMA) Method and a Modified Detrended Fluctuation Analysis (MDFA). We find that CMA is performing equivalently as DFA in long data with weak trends and slightly superior to DFA in short data with weak trends. When comparing standard DFA to MDFA we observe that DFA performs slightly better in almost all examples we studied. We also discuss how several types of trends affect the different types of DFA. For weak trends in the data, the new methods are comparable with DFA in these respects. However, if the functional form of the trend in data is not a-priori known, DFA remains the method of choice. Only a comparison of DFA results, using different detrending polynomials, yields full recognition of the trends. A comparison with independent methods is recommended for proving long-range correlations.

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Section: Further Modifications and Extensions Of Dfamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison of detrending methods for fluctuation analysis

Bashan

Bartsch

Kantelhardt

et al. 2008

Physica A: Statistical Mechanics and its Applications

290

187

View full text Add to dashboard Cite

show abstract

“…Progressive detrended fluctuation analysis is a closely related method, derived from DFA by Staudacher et al [17] in order to distinguish between different phases of sleep in a patient from the heart rate monitoring signal. The difference is in how the signal is segmented.…”

Section: Pdfamentioning

confidence: 99%

A Comparison of On-Engine Surge Detection Algorithms using Knock Accelerometers

Kerres¹,

Cronhjort²,

Mihăescu³

et al. 2017

SAE Technical Paper Series

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On-engine surge detection could help in reducing the safety margin towards surge, thus allowing higher boosting pressures and ultimately low-end torque. In this paper, experimental data from a truck turbocharger compressor mounted on the engine is investigated. A short period of compressor surge is provoked through a sudden, large drop in engine load. The compressor housing is equipped with knock accelerometers. Different signal treatments are evaluated for their suitability with respect to on-engine surge detection: the signal root mean square, the power spectral density in the surge frequency band, the recently proposed Hurst exponent, and a closely related concept optimized to detect changes in the underlying scaling behavior of the signal. For validation purposes, a visual observation of the air filter vibrations are also used to diagnose surge. The four signal treatments are compared with respect to their reliability as surge indicator and the time delay between surge onset and indication. Results show that the signal power in the surge frequency band has reasonably good properties as surge indicator. The normal Hurst exponent is problematic, since periodic vibrations from engine firing dominate the scaling behavior. Root mean square and the above mentioned scaling exponent do not measure vibrations caused by surge directly, but rather the reduction in housing vibrations due to the engine load drop; nevertheless, it was found to be possible to design an indicator that gives good results based on the change in scaling behavior.

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“…Illustrative examples include medical applications in Belisle et al (1998) where the effect of a certain stimulus on neuron behavior is studied and in Staudacher et al (2005) where heart beat variability during sleep is monitored. Osanaiye and Talabi (1989) and Baron (2001) describe applications to detect disease outbreaks.…”

Section: Introductionmentioning

confidence: 99%