New Estimators and Guidelines for Better Use of Fetal Heart Rate Estimators with Doppler Ultrasound Devices

Voicu, Iulian; Ménigot, Sébastien; Kouamé, Denis; Girault, Jean‐Marc

doi:10.1155/2014/784862

Cited by 22 publications

(17 citation statements)

References 20 publications

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“…This system, which transmits ultrasound waves of 2.25 MHz for an acoustic power limited to 1 mW/cm 2 (for more details see [28,29]), was developed to measure both the FHR and fetal movements (pseudo-breathing and limb movements).…”

Section: Data Collectionmentioning

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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Section: Data Collectionmentioning

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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“…The latter were acquired using a homemade pulse Doppler system co-developed with Altaïs Technologies (Tours, France). This Doppler fetal monitor transmits ultrasound waves of 2.25 MHz for an acoustic power limited to 1 mW/cm

(for more details, see [ 11 ]). It was developed to measure both the FHR and fetal movements (pseudo-breathing, limb movements).…”

Section: Data Processedmentioning

confidence: 99%

Centered and Averaged Fuzzy Entropy to Improve Fuzzy Entropy Precision

Girault

Humeau-Heurtier

2018

Entropy

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Several entropy measures are now widely used to analyze real-world time series. Among them, we can cite approximate entropy, sample entropy and fuzzy entropy (FuzzyEn), the latter one being probably the most efficient among the three. However, FuzzyEn precision depends on the number of samples in the data under study. The longer the signal, the better it is. Nevertheless, long signals are often difficult to obtain in real applications. This is why we herein propose a new FuzzyEn that presents better precision than the standard FuzzyEn. This is performed by increasing the number of samples used in the computation of the entropy measure, without changing the length of the time series. Thus, for the comparisons of the patterns, the mean value is no longer a constraint. Moreover, translated patterns are not the only ones considered: reflected, inversed, and glide-reflected patterns are also taken into account. The new measure (so-called centered and averaged FuzzyEn) is applied to synthetic and biomedical signals. The results show that the centered and averaged FuzzyEn leads to more precise results than the standard FuzzyEn: the relative percentile range is reduced compared to the standard sample entropy and fuzzy entropy measures. The centered and averaged FuzzyEn could now be used in other applications to compare its performances to those of other already-existing entropy measures.

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“…From these pre-processing, FAP can be estimated using time and/or frequency techniques, as shown in [13], [14], [15], [16] and references therein.…”

Section: Ultrasound Doppler Signal Parametersmentioning

confidence: 99%

Compressive-sensing-based multidimensional Doppler signal analysis for fetal activity monitoring

Basarab

Quinsac

Girault³

et al. 2014

2014 IEEE International Ultrasonics Symposium

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New Estimators and Guidelines for Better Use of Fetal Heart Rate Estimators with Doppler Ultrasound Devices

Cited by 22 publications

References 20 publications

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

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

Centered and Averaged Fuzzy Entropy to Improve Fuzzy Entropy Precision

Compressive-sensing-based multidimensional Doppler signal analysis for fetal activity monitoring

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