Classification of foetal heart rate sequences based on fractal features

Felgueiras, C. S.; Sá, Joaquim P. Marques de; Bernardes, João; Gama, S.

doi:10.1007/bf02510743

Cited by 28 publications

(20 citation statements)

References 10 publications

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“…[27][28][29][30][31][32][33][34][35][36]. Mostly used nonlinear methods are fractal dimension [18,19,27] and entropy based regularity measurements (approximate entropy and sample entropy) [20,28,29,31]. Recently we have used complex correlation measure (CCM), which measures the variability in the temporal structure of Poincaré, along with standard descriptors (SD1 and SD2) to investigate the changes in dynamics and variability of fHRV time-series with GA [37].…”

Section: Introductionmentioning

confidence: 99%

“…Since recent studies [22][23][24][25] have demonstrated that the recommendation standards for adult heart rate variability analysis [26] cannot be directly applied to fHRV studies, new linear and nonlinear parameters are started to be tested to analyze growth of fetus, classifying abnormal fHR and fetal status etc. [27][28][29][30][31][32][33][34][35][36]. Mostly used nonlinear methods are fractal dimension [18,19,27] and entropy based regularity measurements (approximate entropy and sample entropy) [20,28,29,31].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation

Karmakar

Kimura

Palaniswami

et al. 2015

Biomedical Signal Processing and Control

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a b s t r a c tIn this study, we have investigated the evidence of fetal heart rate asymmetry and how the fetal heart rate asymmetry changes before and after 35 weeks of gestation. Noninvasive fetal electrocardiogram (fECG) signals from 45 pregnant women at the gestational age from16 to 41 weeks with normal single pregnancies were analysed. A nonlinear parameter called heart rate asymmetry (HRA) index that measures time asymmetry of RR interval time-series signal was used to understand the changes of HRA in early and late fetus groups. Results indicate that fetal HRA measured by Porta's Index (PI) consistently increases after 35 weeks gestation compared to foetus before 32 weeks of gestation. It might be due to significant changes of sympatho-vagal balance towards delivery with more sympathetic surge. On the other hand, Guzik's Index (GI) showed a mixed effect i.e., increases at lower lags and decreases at higher lags. Finally, fHRA could potentially help identify normal and the pathological autonomic nervous system development.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation

Karmakar

Kimura

Palaniswami

et al. 2015

Biomedical Signal Processing and Control

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“…Among the possible complexity descriptors, fractal [4][5][6][7], multifractal [8][9][10][11][12], recurrent [1,13] and entropy [14][15][16][17][18] indicators are undoubtedly the most effective.…”

Section: Introductionmentioning

confidence: 99%

n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity: Application to fetal heart rate signals

Zaylaa

Oudjemia

Charara

et al. 2015

Computers in Biology and Medicine

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This paper presents two new concepts for discrimination of signals of different complexity. The first focused initially on solving the problem of setting entropy descriptors by varying the pattern size instead of the tolerance. This led to the search for the optimal pattern size that maximized the similarity entropy. The second paradigm was based on the n-order similarity entropy that encompasses the 1-order similarity entropy. To improve the statistical stability, n-order fuzzy similarity entropy was proposed. Fractional Brownian motion was simulated to validate the different methods proposed, and fetal heart rate signals were used to discriminate normal from abnormal fetuses. In all cases, it was found that it was possible to discriminate time series of different complexity such as fractional Brownian motion and fetal heart rate signals. The best levels of performance in terms of sensitivity (90%) and specificity (90%) were obtained with the n-order fuzzy similarity entropy. However, it was shown that the optimal pattern size and the maximum similarity measurement were related to intrinsic features of the time series.

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“…In our previous study, using fractal analysis as developed by Higuchi [27] for a time series analysis, we found that the FHR sequences have two characteristic scaling regions [28] . Felgueiras et al [29] also reported two scaling regions. Gough [30] found low-and high-frequency FHR variations based on observations of heart beat-to-beat intervals plotted sequentially.…”

Section: Discussionmentioning

confidence: 80%

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

Kikuchi

Unno²,

Kozuma³

et al. 2007

Gynecol Obstet Invest

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Background: Detrended fluctuation analysis (DFA) has recently been validated as an excellent method by which to analyze heart rate variability and distinguish healthy subjects from patients with various types of the cardiac nervous system dysfunction. Methods: One hundred and nineteen fetal heart rate (FHR) recordings obtained from healthy normal fetuses and 68 recordings obtained from small-for-gestational-age (SGA) fetuses were analyzed by DFA to examine gestational and pathologic changes of the scaling exponent, α. Results: In normal fetuses, a significant increase was observed in both the short-term (≤30 s) α₁ and long-term (>30 s) α₂ scaling exponents according to gestational age. The α₁ values of SGA fetuses were not significantly different from those of healthy normal fetuses; however, the α₂ values of the former group (0.955 ± 0.152) were significantly higher than those of normal subjects (0.887 ± 0.128; p = 0.001). Conclusion: The α₂ exponent appears to be a sensitive probe for detecting subtle, and possibly important, changes that occur in fetuses with intrauterine growth restriction, and may be helpful in the early and noninvasive detection of placental insufficiency or incipient intrauterine growth restriction. The use of DFA techniques offers great promise for understanding FHR behavior.

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Classification of foetal heart rate sequences based on fractal features

Cited by 28 publications

References 10 publications

Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation

Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation

n-Order and maximum fuzzy similarity entropy for discrimination of signals of different complexity: Application to fetal heart rate signals

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

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