Fetal heart rate baseline estimation with analysis of fetal movement signal

Abstract

In order to assign appropriate baseline estimation algorithms to different fetal heart rate tracing, a method to evaluate the fetal heart rate (FHR) baseline combining with the fetal movement information was proposed. Fetal actography and tocography were used to extract the fetal movement information. The results showed that the combined method, where the fetal movement detection result was the union of results of actography and tocography, achieved a better performance with the highest sensitivity and an acceptable positive predictive value (PPV). Furthermore, the mean absolute errors (MAEs) of basal FHR values between the two algorithms and the expert were calculated with respect to the duration coefficient of fetal movement. The results showed that the algorithm using empirical mode decomposition (EMD) and Kohonen neural network (KNN) had lower MAEs than a traditional linear baseline estimation algorithm as the duration coefficient increased. However, if the duration coefficient is below 0.2, the errors may be tolerant for the FHR baseline estimation by a linear baseline estimation algorithm, which indicates that different algorithms may be selected for FHR baseline estimation based on different duration coefficients of fetal movement.

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

“…The empirical mode decomposition (EMD) [48], which corresponds to a previous version of this technique, has been used in different biomedical engineering approaches [49]. Particularly, in FHR signal analysis it has been used for FHR estimation from Doppler Ultrasound signals [57], analysis of the FHR signal components in order test the reliability of the EMD performance [58], FHR baseline estimation with analysis of fetal movements [59], assessment of the high frequency FHR information [60], enhancement of the CTG signal quality by reducing signal artifacts [50], and FHR signal feature extraction and classification [49,61]. These works have concluded that EMD could be appropriate for the analysis of the FHR signal components in the time-domain.…”

Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment

“…The previous version of this technique, empirical mode decomposition (EMD), has been used to analyze data in different fields of biomedical engineering [52]. Particularly, in CTG signal analysis EMD has been utilized for FHR estimation from Doppler Ultrasound signals by using EMD-kurtosis method [61], analysis of the FHR signal components in order test the reliability of the EMD performance by using simulated FHR signals [62], FHR baseline estimation with analysis of fetal movements by using EMD and Kohonen neural network [63], assessment of the high frequency information of FHR in different conditions of fetal activity [64], enhancement of the CTG signal quality by reducing signal artifacts [53], and FHR signal feature extraction and classification by using EMD and SVM [52], [65]. These works have concluded that EMD could be suited for the extraction and analysis of the FHR signal components in the time-domain.…”

Automated Detection of Fetal Movements Based on Baseline Calculation and Fetal Heart Rate Accelerations

“…The experimental data cotains 42 pairs of signals selected from the clinical data from the People's Hospital of Baiyun District, Guangzhou (Lu et al, 2014), including antenatal FHR, TOCO and fetal movement tracing. Each signal contains more than 20 minutes of monitoring data.…”

“…All of these methods above focused more on signal acquisition and improving the signal quality. Lu et al (2014) used Ostu method to obtain the fixed threshold automatically for fetal movement detection, which focused on the methodology of detection from actography. However, fixed threshold cannot adapt to local variation of fetal movement signals.…”