An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram
An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram
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DOI：
10.3390/s20133757
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发表时间：
2020-07-01
期刊：
影响因子：
3.9
通讯作者：
Cao, Hung
中科院分区：
文献类型：
--
作者：
Vo, Khuong;Le, Tai;Cao, Hung
胎儿心电图（fECG）监测的侵入性方法被广泛使用，电极直接附着在胎儿头皮上。有潜在的风险，如感染，因此，它通常是在劳动过程中进行罕见的情况下。电子和技术的最新进展已经使得能够通过从在母亲腹部区域中非侵入性记录的组合胎儿/母体ECG（f/mECG）信号中提取fECG来从妊娠早期进行fECG监测。然而，需要参考母体ECG的繁琐算法以及繁重的特征制作使得日常生活中的门诊外fECG监测尚不可行。为了解决这些挑战，我们提出了一个纯端到端的深度学习模型来检测胎儿QRS波群（即，在胎儿ECG波形上观察到的主要尖峰）。此外，该模型具有残差网络（ResNet）架构，该架构采用新颖的一维倍频程卷积（OctConv）来学习多个时间频率特征，从而降低了内存和计算成本。重要的是，该模型能够突出对检测更突出的区域的贡献。为了评估我们的方法，我们以原始形式使用了来自PhysioNet 2013 Challenge的带有标记QRS波群注释的数据，然后用高斯和运动噪声修改了数据，模仿了真实世界的场景。该模型的F（1）得分为91.1%，同时能够节省超过50%的计算成本，性能下降不到2%，证明了我们的方法的有效性。
The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F(1)score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.