G D H Croaker

Publications (3)3.75 Total impact

• Article: Detrended fluctuation analysis: a suitable method for studying fetal heart rate variability?

  J C Echeverría, B R Hayes-Gill, J A Crowe, M S Woolfson, G D H Croaker
  [show abstract] [hide abstract]
  ABSTRACT: We evaluate the suitability of an enhanced detrended fluctuation analysis for studying fetal heart rate series involving imperfect quality of information. Our results indicate that to explore persistent long-range correlations, or fractality, the collection requirements of the data can be relaxed by allowing the possibility of using averaged fetal heart rate series. In addition, it also appears feasible to employ, without producing major alterations in the long-range scaling behaviour, fragmented fetal heart rate series involving up to 50% of random missing values, or up to 50 min of consecutive missing samples in recordings of approximately equal to 8 h length. These are crucial advantages to overcome the often variable quality of fetal data. Consequently, these findings may open the possibility of obtaining information concerning the development of neural processes from fetal heart rate series, despite their non-stationary and fragmented nature.
  Physiological Measurement 07/2004; 25(3):763-74. · 1.68 Impact Factor
• Source

  Available from: Harish Vyas

  Article: Interpretation of heart rate variability via detrended fluctuation analysis and alphabeta filter.

  J C Echeverría, M S Woolfson, J A Crowe, B R Hayes-Gill, G D H Croaker, H Vyas
  [show abstract] [hide abstract]
  ABSTRACT: Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability data. In this paper, we present the incorporation of the alphabeta filter to DFA to determine patterns in the power-law behavior that can be found in these correlations. Well-known simulated scenarios and real data involving normal and pathological circumstances were used to evaluate this process. The results presented here suggest the existence of evolving patterns, not always following a uniform power-law behavior, that cannot be described by scaling exponents estimated using a linear procedure over two predefined ranges. Instead, the power law is observed to have a continuous variation with segment length. We also show that the study of these patterns, avoiding initial assumptions about the nature of the data, may confer advantages to DFA by revealing more clearly abnormal physiological conditions detected in congestive heart failure patients related to the existence of dominant characteristic scales.
  Chaos An Interdisciplinary Journal of Nonlinear Science 07/2003; 13(2):467-75. · 2.08 Impact Factor
• Source

  Available from: Harish Vyas

  Article: Interpretation of heart rate variability via detrended fluctuation analysis and alpha-beta filter

  J. C. Echeverria, M S Woolfson, J A Crowe, B R Hayes-Gill, G D H Croaker, H Vyas
  [show abstract] [hide abstract]
  ABSTRACT: Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability data. In this paper, we present the incorporation of the alpha-beta filter to DFA to determine patterns in the power-law behaviour that can be found in these correlations. Well-known simulated scenarios and real data involving normal and pathological circumstances were used to evaluate this process. The results presented here suggest the existence of evolving patterns, not always following a uniform power-law behaviour, that cannot be described by scaling exponents estimated using a linear procedure over two predefined ranges. Instead, the power law is observed to have a continuous variation with segment length. We also show that the study of these patterns, avoiding initial assumptions about the nature of the data, may confer advantages to DFA by revealing more clearly abnormal physiological conditions detected in congestive heart failure patients related to the existence of dominant characteristic scales. Comment: 18 pages, 14 figures
  06/2003;
• Article: Interpretation of heart rate variability via detrended fluctuation analysis and �� filter

  J. C. Echeverr ´ õa, M. S. Woolfson, J. A. Crowe, B. R. Hayes-Gill, G. D. H. Croaker, H. Vyas
  [show abstract] [hide abstract]
  ABSTRACT: Detrended fluctuation analysis (DFA), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability data. In this paper, we present the incorporation of the �� filter to DFA to determine patterns in the power- law behaviour that can be found in these correlations. Well-known simulated scenarios and real data involving normal and pathological circumstances were used to evaluate this process. The results presented here suggest the existence of evolving patterns, not always following a uniform power- law behaviour, that cannot be described by scaling exponents estimated using a linear procedure over two predefined ranges. Instead, the power law is observed to have a continuous variation with segment length. We also show that the study of these patterns, avoiding initial assumptions about the nature of the data, may confer advantages to DFA by revealing more clearly abnormal physiological conditions detected in congestive heart failure patients related to the existence of dominant characteristic scales. Detrended fluctuation analysis (DFA) (1), suitable for the analysis of nonstationary time series, has confirmed the existence of persistent long-range correlations in healthy heart rate variability (HRV) data (2). Notwith- standing the early success in the application of DFA, a limitation exists concerning the final stage of the tech- nique as applied to HRV. This is related to the calculation of the power-law scaling exponent for the short-term or long-term range, a range division that has been assumed by the presence of the "crossover phenomenon", or criti- cal segment length where there is a sudden change in the power law, found in several patients (1, 3). We believe that one should not reduce DFA to quantify only two (a short- and a long-range) scaling exponents, but instead determine if there is more information in the power-law behaviour. A previous attempt using coarse local slopes for the scaling exponent has already suggested a kind of scaling stability impairment with heart disease (4); how- ever, a detailed analysis of the patterns of the slope is not provided. Here, we present the incorporation of the �� filter to the DFA for the estimation of the power law as a function of the time scales. The �� filter (5) is a recur- sive least-squares method that has been used for tracking targets or to characterise the operation of an induction motor drive, for example (6, 7). We have employed this filter as a more appropriate approach to obtain a de- tailed analysis of the scaling behaviour by improving the precision in the interpretation of the DFA results. Well- known simulated scenarios and real HRV data involving normal and pathological circumstances were used to eval- uate this enhancement. Our results suggest the existence of evolving scaling patterns not always presenting a uni- form power-law behaviour that can be readily detected by a linear slope estimation procedure over two predefined ranges. We also show that the incorporation of the study of these patterns may confer advantages to the DFA by revealing more clearly abnormal physiological conditions detected in congestive heart failure (CHF) patients re- lated to the existence of dominant characteristic scales.