Fetal heart rate data pre-processing and annotation

Chudáček, Václav; Huptych, Michal; Koucký, Michal; Spilka, Jiří; Bauer, Lukáš; Lhotská, Lenka

doi:10.1109/itab.2009.5394441

Cited by 10 publications

(9 citation statements)

References 15 publications

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“…In clinical practice, during the recording process using Doppler ultrasound, the FHR signal contains many artifacts or spikes due to maternal and fetal movements or transducer displacement [ 1 ]. Therefore, before further analysis, we eliminated noise to obtain a relatively pure signal for more accurate results, as described in Reference [ 18 ]. In this work, we employed a preprocessing algorithm involving three steps.…”

Section: Methodsmentioning

confidence: 99%

A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

Zhao

Zhang

Deng

2018

JCM

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Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.

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Section: Methodsmentioning

confidence: 99%

A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

Zhao

Zhang

Deng

2018

JCM

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“…Before further analysis of the CTG signal, noises are eliminated to obtain reasonably better quality signal for more accurate results. So, the main goal of this step is to reduce the aforementioned noises by applying a conventional preprocessing techniques applied in FHR signal analysis [13,26] and the steps are shown in Fig. 2.…”

Section: Preprocessingmentioning

confidence: 99%

Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study

Daydulo

Thamineni

Dasari

et al. 2022

BMC Med Inform Decis Mak

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Background Clinically cardiotocography is a technique which is used to monitor and evaluate the level of fetal distress. Even though, CTG is the most widely used device to monitor determine the fetus health, existence of high false positive result from the visual interpretation has a significant contribution to unnecessary surgical delivery or delayed intervention. Objective In the current study an innovative computer aided fetal distress diagnosing model is developed by using time frequency representation of FHR signal using generalized Morse wavelet and the concept of transfer learning of pre-trained ResNet 50 deep neural network model. Method From the CTG data that is obtained from the only open access CTU-UHB data base only FHR signal is extracted and preprocessed to remove noises and spikes. After preprocessing the time frequency information of FHR signal is extracted by using generalized Morse wavelet and fed to a pre-trained ResNet 50 model which is fine tuned and configured according to the dataset. Main outcome measures Sensitivity (Se), specificity (Sp) and accuracy (Acc) of the model adopted from binary confusion matrix is used as outcome measures. Result After successfully training the model, a comprehensive experimentation of testing is conducted for FHR data for which a recording is made during early stage of labor and last stage of labor. Thus, a promising classification result which is accuracy of 98.7%, sensitivity of 97.0% and specificity 100% are achieved for FHR signal of 1st stage of labor. For FHR recorded in last stage of labor, accuracy of 96.1%, sensitivity of 94.1% and specificity 97.7% are achieved. Conclusion The developed model can be used as a decision-making aid system for obstetrician and gynecologist.

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“…Each CTG contains a FHR time series and a UC signal, each sampled at 4 Hz. These 552 recordings were screened by Chudacek et al [26] using many clinical and technical criteria, for instance, a maximum of 60 minutes for the first stage of labor and a maximum of 30 minutes for the second stage of labor.…”

Section: A Data Descriptionmentioning

confidence: 99%

“…The total missing percentage Q is increased from 5% to 40%, i.e., Q ∈ [5%, 10%, 20%, 30%, 40%] , and num is adjusted successively as follows: increased from 8 to 64, i.e., num ∈ [8,16,32,48,64]. The reason why Q ≤ 40%is the existing rule of clinical collection: when Q exceeds 40%, this signal is not available [6], [26]. This rule is also applied in the screening of CTU-UHB.…”

Section: A Data Descriptionmentioning

confidence: 99%

“…2) Protocol Two: Fewer but longer missing data segments: In this protocol, Q is set at 5%, i.e., the total length of data missing is 100. Define one of the missing lengths as a long time missing, q max ∈ (25, 100], and adjust num with the mathematical rules of formula (26). The main goal of this protocol is to evaluate the dependence of miniMSDL on the length of each missing q.…”

Section: A Data Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstruction of Missing Samples in Antepartum and Intrapartum FHR Measurements Via Mini-Batch-Based Minimized Sparse Dictionary Learning

Zhang

Zhao

Deng

et al. 2022

IEEE J. Biomed. Health Inform.

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Fetal heart rate data pre-processing and annotation

Cited by 10 publications

References 15 publications

A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

Deep learning based fetal distress detection from time frequency representation of cardiotocogram signal using Morse wavelet: research study

Reconstruction of Missing Samples in Antepartum and Intrapartum FHR Measurements Via Mini-Batch-Based Minimized Sparse Dictionary Learning

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