Fetal cardiotocography monitoring using Legendre neural networks

Abstract

A new technique for electronic fetal monitoring (EFM) using an efficient structure of neural networks based on the Legendre series is presented in this paper. Such a structure is achieved by training a Legendre series-based neural network (LNN) to classify the different fetal states based on recorded cardiotocographic (CTG) data sets given by others. These data sets consist of measurements of fetal heart rate (FHR) and uterine contraction (UC). The applied LNN utilizes a Legendre series expansion for the input vectors and, hence, has the capability to produce explicit equations describing multi-input multi-output systems. Simulations of the proposed technique in EFM demonstrate its high efficiency. Training the LNN requires a few number of iterations (5–10 epochs). The applied technique makes the classification of the fetal state available through equations combining the trained LNN weights and the current measured CTG record. A comparison of performance between the proposed LNN and other popular neural network techniques such as the Volterra neural network (VNN) in EFM is provided. The comparison shows that, the LNN outperforms the VNN in case of less computational requirements and fast convergence with a lower mean square error.

Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring

“…Similarly, in fPCG signals, AI-based algorithms were used to detect fHSs [45,48]. The last area is the classification of fetal health using signals recorded by means of CTG [11,43,[86][87][88][89], but also by means of alternative techniques of fPCG [66,90,91] or fMCG [44]. Figure 4 illustrates the areas where the AI was used in the past marked by the red frame.…”

“…Signal preprocessing, noise suppression [12,13,142,143], and elimination of outliers [13,43] For optimal feature subset selection, the FA [78] or GA [72] have proven to be effective. Many studies [72,86,147,148] proved that lower amount of appropriate features leads to a more accurate classification compared to having a higher number of features that do not carry important information or distort the resulting classification. (normal, abnormal) [147,149,150] or three classes (normal, suspect, pathological) [54,86,87].…”

“…Many studies [72,86,147,148] proved that lower amount of appropriate features leads to a more accurate classification compared to having a higher number of features that do not carry important information or distort the resulting classification. (normal, abnormal) [147,149,150] or three classes (normal, suspect, pathological) [54,86,87].…”

“…Highly accurate classification of fetal state using CTG recordings and the second-order Legendre ANN was designed by Alsayyari [86]. When using 21 attributes, 4 signals out of 1000 were misclassified, with 10 attributes, only 2 signals out of 1000 were misclassified.…”

Intrapartum cardiotocography trace pattern pre-processing, features extraction and fetal health condition diagnoses based on RCOG guideline

“…However, reduced features are needed during the performance of classification. Even though the proposed model is not found in Python-ML techniques, refereeing classification trends should be considered not solely based on models and parameter tuning ( Alsayyari, 2019 ). A hypoxia disclosure by using an external CTG dataset where the evaluation outcomes consider both deep learning and ensemble learning was presented in Nandipati & XinYing (2020) .…”