BACKGROUND Fetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. OBJECTIVE Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions. METHODS In this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML. RESULTS Based on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectively CONCLUSIONS: Once the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.

中文翻译：

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DeepFHR：使用基于卷积神经网络的胎儿心率信号智能预测胎儿酸血症。

背景技术胎儿心率（FHR）监测是妇产科医生用来评估胎儿状态的筛查工具。由于复杂性和非线性，使用通用指南对FHR信号进行可视化解释通常会导致观察者之间和观察者内部的主观差异很大。目的因此，最近已经开发了基于先进人工智能（AI）技术的计算机辅助诊断（CAD）系统，以协助产科医生做出客观的医学决策。方法在这项工作中，我们提出了一个8层深度卷积神经网络（CNN）框架来自动预测胎儿酸血症。经过信号预处理后，使用连续小波变换（CWT）获得输入的二维（2D）图像，这为在时域和频域中观察和捕获FHR信号的隐藏特征信息提供了更好的方法。与传统的机器学习（ML）方法不同，这项工作不需要执行复杂的特征工程，即特征提取和选择。实际上，二维CNN模型可以在不丢失信息功能的前提下，从输入数据中自学有用的功能，这代表了深度学习（DL）相对于ML的巨大优势。结果基于测试开放访问数据库（CTU-UHB），经过全面实验，与其他最新方法相比，我们使用最佳CNN配置获得了更好的分类性能：准确性，敏感性，特异性，