Fetal heart rate baseline computation with a weighted median filter

Boudet, Samuel; l’Aulnoit, Agathe Houzé de; Demailly, Romain; Peyrodie, Laurent; Beuscart, R.; L'aulnoit, D. Houze De

doi:10.1016/j.compbiomed.2019.103468

Cited by 29 publications

(34 citation statements)

References 25 publications

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“…The number of articles probing the use of non-linear measures to assess the fHR signals analysis has been growing in the past decade. Non-linear analysis has been successfully applied in the study of fetal heart rate with several research objectives, such as fetal maturation or gestational age (197)(198)(199), fetal gender (182,200), labor stages (201,202), cesarean section (51,203), preterm birth (80,204), impact of nuchal cord on antenatal (205), fHR baseline (206), behavioral state (207,208), IUGR (209)(210)(211)(212)(213), hypoxia (128,137,214,215), fetal distress (216)(217)(218), maternal pathologies (219,220), and signal processing (198,(221)(222)(223)(224). Therefore, it is important that the scientific community is aware of the non-linear methods used depending on the research objective.…”

Section: Discussionmentioning

confidence: 99%

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

et al. 2021

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The analysis of fetal heart rate variability has served as a scientific and diagnostic tool to quantify cardiac activity fluctuations, being good indicators of fetal well-being. Many mathematical analyses were proposed to evaluate fetal heart rate variability. We focused on non-linear analysis based on concepts of chaos, fractality, and complexity: entropies, compression, fractal analysis, and wavelets. These methods have been successfully applied in the signal processing phase and increase knowledge about cardiovascular dynamics in healthy and pathological fetuses. This review summarizes those methods and investigates how non-linear measures are related to each paper's research objectives. Of the 388 articles obtained in the PubMed/Medline database and of the 421 articles in the Web of Science database, 270 articles were included in the review after all exclusion criteria were applied. While approximate entropy is the most used method in classification papers, in signal processing, the most used non-linear method was Daubechies wavelets. The top five primary research objectives covered by the selected papers were detection of signal processing, hypoxia, maturation or gestational age, intrauterine growth restriction, and fetal distress. This review shows that non-linear indices can be used to assess numerous prenatal conditions. However, they are not yet applied in clinical practice due to some critical concerns. Some studies show that the combination of several linear and non-linear indices would be ideal for improving the analysis of the fetus's well-being. Future studies should narrow the research question so a meta-analysis could be performed, probing the indices' performance.

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Section: Discussionmentioning

confidence: 99%

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

et al. 2021

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“…It is approximately equivalent to the sum of local variability (the standard deviation of FHR over 1 minute) and the amplitude of the event (the difference between the average FHR and the baseline). α is a constant (set to 3 bpm) added to be able to tolerate a minimum when the variability in the FHR was null or almost null 33 …”

Section: Methodsmentioning

confidence: 99%

“…31 These invalid data points were detected, removed, and finally replaced by linearly interpolated values between the valid data points. 32 Invalid data points were defined as (1) signal values outside the acceptable range of 50-220 bpm 33 and (2) sudden large deviations (i.e., an absolute value of over 25 bpm at two adjacent points) in the FHR. 13,33 The preprocessed FHR signal was then compared with the original one (Fig.…”

Section: Fhr Signal Pre-processingmentioning

confidence: 99%

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CTGNet: Automatic Analysis of Fetal Heart Rate from Cardiotocograph Using Artificial Intelligence

Zhong

Hao

Fan

et al. 2022

Maternal-Fetal Medicine

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This study investigates the efficacy of analyzing fetal heart rate (FHR) signals based on Artificial Intelligence to obtain a baseline calculation and identify accelerations/decelerations in the FHR through electronic fetal monitoring during labor.Methods: A total of 43,888 cardiotocograph(CTG) records of female patients in labor from January 2012 to December 2020 were collected from the NanFang Hospital of Southern Medical University. After filtering the data, 2341 FHR records were used for the study. The ObVue fetal monitoring system, manufactured by Lian-Med Technology Co. Ltd., was used to monitor the FHR signals for these pregnant women from the beginning of the first stage of labor to the end of delivery. Two obstetric experts together annotated the FHR signals in the system to determine the baseline as well as accelerations/ decelerations of the FHR. Our cardiotocograph network (CTGNet) as well as traditional methods were then used to automatically analyze the baseline and acceleration/deceleration of the FHR signals. The results of calculations were compared with the annotations provided by the obstetric experts, and ten-fold cross-validation was applied to evaluate them. The root-mean-square difference (RMSD) between the baselines, acceleration F-measure (Acc.F-measure), deceleration F-measure (Dec.F-measure), coefficient of synthetic inconsistency (SI) and the morphological analysis discordance index (MADI) were used as evaluation metrics. The data were analyzed by using a paired t-test.Results: The proposed CTGNet was superior to the best traditional method, proposed by Mantel, in terms of the RMSD.BL (1.7935 ± 0.8099 vs. 2.0293 ± 0.9267, t = À3.55 , P = 0.004),

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“…Median filtering is a low-frequency enhanced spatial domain filtering technique. In this paper, the four-point median filtering principle is used to implement the linear filter [15]. The specific mathematical expression is shown in Equation (2).…”

Section: Four-point Median Filteringmentioning

confidence: 99%

Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM

Huang

et al. 2020

Sensors

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Action recognition algorithms are widely used in the fields of medical health and pedestrian dead reckoning (PDR). The classification and recognition of non-normal walking actions and normal walking actions are very important for improving the accuracy of medical health indicators and PDR steps. Existing motion recognition algorithms focus on the recognition of normal walking actions, and the recognition of non-normal walking actions common to daily life is incomplete or inaccurate, resulting in a low overall recognition accuracy. This paper proposes a microelectromechanical system (MEMS) action recognition method based on Relief-F feature selection and relief-bagging-support vector machine (SVM). Feature selection using the Relief-F algorithm reduces the dimensions by 16 and reduces the optimization time by an average of 9.55 s. Experiments show that the improved algorithm for identifying non-normal walking actions has an accuracy of 96.63%; compared with Decision Tree (DT), it increased by 11.63%; compared with k-nearest neighbor (KNN), it increased by 26.62%; and compared with random forest (RF), it increased by 11.63%. The average Area Under Curve (AUC) of the improved algorithm improved by 0.1143 compared to KNN, by 0.0235 compared to DT, and by 0.04 compared to RF.

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Fetal heart rate baseline computation with a weighted median filter

Cited by 29 publications

References 25 publications

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

Non-linear Methods Predominant in Fetal Heart Rate Analysis: A Systematic Review

CTGNet: Automatic Analysis of Fetal Heart Rate from Cardiotocograph Using Artificial Intelligence

Recognition of Common Non-Normal Walking Actions Based on Relief-F Feature Selection and Relief-Bagging-SVM

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