Detrended Fluctuation Analysis of Heart Rate Variability in Normal and Growth-Restricted Fetuses

Kikuchi, Akihiko; Unno, Nobuya; Kozuma, Shiro; Taketani, Yuji

doi:10.1159/000109266

Cited by 14 publications

(3 citation statements)

References 42 publications

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“…• In IUGR fetuses reduced HRV indices were shown that correspond to lAMP using CTG recordings (Nijhuis et al 2000). Decreased HRV complexity in IUGR fetuses has been described in CTG recordings (lyapunov exponent (LE) (Kikuchi et al 2008), Lempel Ziv complexity (LZC) and multiscale entropy (MSE) of approximate entropy (ApEn) and sample entropy (SampEn) (Ferrario et al 2009)) across the age range from 27 to 34 WGA. It should be noted that these indices of complexity, obtained from the CTG based heart rate signal, correspond to long-term behavior.…”

Section: Discussionmentioning

confidence: 90%

Heart rate variability categories of fluctuation amplitude and complexity: diagnostic markers of fetal development and its disturbances

et al. 2019

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Fetal development and its disturbances have been reported to be associated with a multiplicity of HRV indices. Furthermore, these HRV indices change with maturation. We propose the abstraction of HRV categories defined by short-and long-term fluctuation amplitude, complexity, and pattern indices that cover all relevant aspects of maturational age, behavioral influences and a series of pathological disturbances. The study data are provided by multiple centers. Our approach is an important step towards the goal of a standardized diagnostic tool for early identification of fetal developmental disturbances with respect to the reduction of serious complications in the later life.

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Section: Discussionmentioning

confidence: 90%

Heart rate variability categories of fluctuation amplitude and complexity: diagnostic markers of fetal development and its disturbances

et al. 2019

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“…In a later study Schneider et al, reported a difference in SDNN and RMSSD where one-third of the IUGR study population included compromised fetuses with only segments from the quiet sleep state analyzed [17]. Further, Detrended Fluctuation Analysis (DFA) of fetal heart-rate tracings has shown that, even though the normal fetuses’ short-term (α 1 ) scaling exponent is not significantly different from that of IUGR fetuses or those deemed small for gestational age (SGA), the long-term (α 2 ) scaling exponent of IUGR/SGA were significantly higher than that of normal fetus, indicating that heart-rate dynamics of IUGR fetuses are different from those of normal healthy fetus [25]. This implies that DFA shows the difference between low-risk and IUGR/SGA fetuses only in low-frequency components but not in high-frequency components.…”

Section: Discussionmentioning

confidence: 99%

Differences in the sleep states of IUGR and low-risk fetuses: An MCG study

Sriram

Mencer

McKelvey

et al. 2013

Early Human Development

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Background Intrauterine growth restriction (IUGR) is a fetal condition characterized by growth-rate reduction. Afflicted fetuses tend to display abnormalities in heart rate. Objective To study the differences in the heart-rate variability of low-risk fetuses and IUGR fetuses during different behavioral states. Methods A total of 40 fetal magnetocardiograms were recorded from 20 low-risk and 20 IUGR fetuses using a 151-sensor SQUID-array system. The maternal cardiac signals were attenuated using signal-space projection. Fetal R waves were identified using an adaptive Hilbert transform approach and fetal heart rate calculated. In each three-minute window, the heart rate was classified into patterns reflective of quiet sleep (pattern A) and active sleep (pattern B) using the criteria of Nijhuis. Two adjacent 3-minute windows exhibiting the same pattern were selected for analysis from every dataset. Heart-rate variability in that 6-minute window was characterized using three measures, Standard Deviation of Normal to Normal (SDNN), Root Mean Square of Successive Differences (RMSSD) and Phase Plane Area (PPA). Results All three measures tended to be lower in the IUGR group compared to the low-risk group. However, when the measures were analyzed in patterns, only PPA showed significant difference between the risk groups in Pattern A, where as both PPA and SDNN showed highly significant risk-group differences in Pattern B. RMSSD did not show any significant risk-group difference. Conclusion The result signifies that the heart-rate variability of IUGR fetuses is different from that of low-risk fetuses, and only PPA was able to capture the HRV differences in both quiet and active states. The difference between these two groups of fetuses shows that the fetal-activity states are potential confounders when characterizing heart-rate variability.

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“…The maximum Lyapunov exponent (MLE) as an estimate of predictability of a dynamic system, was reported to be significantly lower for IUGR cases than control (Kikuchi et al , 2006). The authors also showed Detrend Fluctuation Analysis (DFA) can be used to separate IUGR cases from normal controls (Kikuchi et al , 2008). It was further supported by Ferrario et al, who found significantly higher DFA values of long-term scaling exponents for IUGR cases (Ferrario et al , 2009b).…”

Section: Application Of 1d-dus In Pathological Conditionsmentioning

confidence: 99%

Cardiotocography and beyond: a review of one-dimensional Doppler ultrasound application in fetal monitoring

2018

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One-dimensional Doppler ultrasound (1D-DUS) provides a low-cost and simple method for acquiring a rich signal for use in cardiovascular screening. However, despite the use of 1D-DUS in cardiotocography (CTG) for decades, there are still challenges that limit the effectiveness of its users in reducing fetal and neonatal morbidities and mortalities. This is partly due to the noisy, transient, complex and nonstationary nature of the 1D-DUS signals. Current challenges also include lack of efficient signal quality metrics, insufficient signal processing techniques for extraction of fetal heart rate and other vital parameters with adequate temporal resolution, and lack of appropriate clinical decision support for CTG and Doppler interpretation. Moreover, the almost complete lack of open research in both hardware and software in this field, as well as commercial pressures to market the much more expensive and difficult to use Doppler imaging devices, has hampered innovation. This paper reviews the basics of fetal cardiac function, 1D-DUS signal generation and processing, its application in fetal monitoring and assessment of fetal development and wellbeing. It also provides recommendations for future development of signal processing and modeling approaches, to improve the application of 1D-DUS in fetal monitoring, as well as the need for annotated open databases.

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Detrended Fluctuation Analysis of Heart Rate Variability in Normal and Growth-Restricted Fetuses

Cited by 14 publications

References 42 publications

Heart rate variability categories of fluctuation amplitude and complexity: diagnostic markers of fetal development and its disturbances

Heart rate variability categories of fluctuation amplitude and complexity: diagnostic markers of fetal development and its disturbances

Differences in the sleep states of IUGR and low-risk fetuses: An MCG study

Cardiotocography and beyond: a review of one-dimensional Doppler ultrasound application in fetal monitoring

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