A deep learning approach for fetal QRS complex detection
A deep learning approach for fetal QRS complex detection
复制标题
胎儿 QRS 波群检测的深度学习方法
DOI：
10.1088/1361-6579/aab297
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发表时间：
2018-04-01
影响因子：
3.2
通讯作者：
Wang, Guoli
中科院分区：
文献类型：
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作者：
Zhong, Wei;Liao, Lijuan;Wang, Guoli
目的：无创性胎儿心电图（NI-FECG）有可能为胎儿疾病的检测和诊断提供更多的临床信息。我们提出并演示了一种通过使用卷积神经网络（CNN）模型从原始NI-FECG信号中检测胎儿QRS波群的深度学习方法。主要目的是研究是否仍然可以从单通道NI-FECG信号的特征获得可靠的胎儿QRS波群检测性能，而不消除母体ECG（MECG）信号。方法：提出了一种用于识别胎儿QRS波群的深度学习方法。首先，我们从心脏病学挑战数据库中的PhysioNet/computing收集数据。将样本熵方法用于信号质量评价。在进一步的分析中排除了部分质量差的信号。其次，在所提出的方法中，原始NI-FECG信号的特征在被馈送到CNN分类器以执行胎儿QRS波群检测之前被归一化。我们使用精确度，召回率，F-测量和准确性作为评估指标来评估胎儿QRS波群检测的性能。主要结果：与其他三种知名的模式分类方法（即KNN，朴素贝叶斯和SVM）相比，所提出的深度学习方法可以实现相对较高的精确度（75.33%），召回率（80.54%）和F-测量分数（77.85%）。重要性：所提出的深度学习方法可以在不消除MECG信号的情况下从原始NI-FECG信号获得可靠的胎儿QRS波群检测性能。此外，不同的激活函数和信号质量评估对分类性能的影响进行了评估，结果表明，Relu优于Sigmoid和Tanh在这个特定的任务，并在本研究中的信号质量评估步骤获得更好的分类性能。
Objective: Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide more additional clinical information for detecting and diagnosing fetal diseases. We propose and demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals by using a convolutional neural network (CNN) model. The main objective is to investigate whether reliable fetal QRS complex detection performance can still be obtained from features of single-channel NI-FECG signals, without canceling maternal ECG (MECG) signals. Approach: A deep learning method is proposed for recognizing fetal QRS complexes. Firstly, we collect data from set-a of the PhysioNet/computing in Cardiology Challenge database. The sample entropy method is used for signal quality assessment. Part of the bad quality signals is excluded in the further analysis. Secondly, in the proposed method, the features of raw NI-FECG signals are normalized before they are fed to a CNN classifier to perform fetal QRS complex detection. We use precision, recall, F-measure and accuracy as the evaluation metrics to assess the performance of fetal QRS complex detection. Main results: The proposed deep learning method can achieve relatively high precision (75.33%), recall (80.54%), and F-measure scores (77.85%) compared with three other well-known pattern classification methods, namely KNN, naive Bayes and SVM. Significance: the proposed deep learning method can attain reliable fetal QRS complex detection performance from the raw NI-FECG signals without canceling MECG signals. In addition, the influence of different activation functions and signal quality assessment on classification performance are evaluated, and results show that Relu outperforms the Sigmoid and Tanh on this particular task, and better classification performance is obtained with the signal quality assessment step in this study.