Zhao Zhidong, Zhang Yang, Comert Zafer, Deng Yanjun
Hangdian Smart City Research Center of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, China.
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China.
Front Physiol. 2019 Mar 12;10:255. doi: 10.3389/fphys.2019.00255. eCollection 2019.
Electronic fetal monitoring (EFM) is widely applied as a routine diagnostic tool by clinicians using fetal heart rate (FHR) signals to prevent fetal hypoxia. However, visual interpretation of the FHR usually leads to significant inter-observer and intra-observer variability, and false positives become the main cause of unnecessary cesarean sections. The main aim of this study was to ensure a novel, consistent, robust, and effective model for fetal hypoxia detection. In this work, we proposed a novel computer-aided diagnosis (CAD) system integrated with an advanced deep learning (DL) algorithm. For a 1-dimensional preprocessed FHR signal, the 2-dimensional image was transformed using recurrence plot (RP), which is considered to greatly capture the non-linear characteristics. The ultimate image dataset was enriched by changing several parameters of the RP and was then used to feed the convolutional neural network (CNN). Compared to conventional machine learning (ML) methods, a CNN can self-learn useful features from the input data and does not perform complex manual feature engineering (i.e., feature extraction and selection). Finally, according to the optimization experiment, the CNN model obtained the average performance using optimal configuration across 10-fold: accuracy = 98.69%, sensitivity = 99.29%, specificity = 98.10%, and area under the curve = 98.70%. To the best of our knowledge, this approached achieved better classification performance in predicting fetal hypoxia using FHR signals compared to the other state-of-the-art works. In summary, the satisfied result proved the effectiveness of our proposed CAD system for assisting obstetricians making objective and accurate medical decisions based on RP and powerful CNN algorithm.
电子胎儿监护（EFM）被临床医生广泛用作常规诊断工具，通过胎儿心率（FHR）信号来预防胎儿缺氧。然而，对FHR的视觉解读通常会导致显著的观察者间和观察者内差异，假阳性成为不必要剖宫产的主要原因。本研究的主要目的是确保建立一个新颖、一致、稳健且有效的胎儿缺氧检测模型。在这项工作中，我们提出了一种集成先进深度学习（DL）算法的新型计算机辅助诊断（CAD）系统。对于一维预处理后的FHR信号，使用递归图（RP）将其转换为二维图像，RP被认为能极大地捕捉非线性特征。通过改变RP的几个参数来丰富最终的图像数据集，然后将其用于输入卷积神经网络（CNN）。与传统机器学习（ML）方法相比，CNN可以从输入数据中自学习有用特征，而无需进行复杂的手动特征工程（即特征提取和选择）。最后，根据优化实验，CNN模型在10折交叉验证中使用最优配置获得了平均性能：准确率 = 98.69%，灵敏度 = 99.29%，特异度 = 98.10%，曲线下面积 = 98.70%。据我们所知，与其他现有技术相比，该方法在使用FHR信号预测胎儿缺氧方面取得了更好的分类性能。总之，满意的结果证明了我们提出的CAD系统基于RP和强大的CNN算法辅助产科医生做出客观准确医疗决策的有效性。
