Alsayyari Abdulaziz
Computer Engineering Department, Shaqra University, Dawadmi 11911, Ar Riyadh, Saudi Arabia.
Biomed Tech (Berl). 2019 Dec 18;64(6):669-675. doi: 10.1515/bmt-2018-0074.
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.
本文提出了一种基于勒让德级数的高效神经网络结构的电子胎儿监护（EFM）新技术。这种结构是通过训练基于勒让德级数的神经网络（LNN）来实现的，该网络根据他人提供的记录的产程图（CTG）数据集对不同的胎儿状态进行分类。这些数据集包括胎儿心率（FHR）和子宫收缩（UC）的测量值。应用的LNN对输入向量使用勒让德级数展开，因此有能力生成描述多输入多输出系统的显式方程。在EFM中对所提出技术的仿真证明了其高效率。训练LNN需要少量的迭代（5 - 10个轮次）。应用的技术通过结合训练后的LNN权重和当前测量的CTG记录的方程实现胎儿状态的分类。提供了所提出的LNN与EFM中其他流行的神经网络技术（如沃尔泰拉神经网络（VNN））之间的性能比较。比较表明，在计算要求较低且收敛速度快且均方误差较低的情况下，LNN优于VNN。
