MS-HNN: Multi-Scale Hierarchical Neural Network With Squeeze and Excitation Block for Neonatal Sleep Staging Using a Single-Channel EEG
MS-HNN: Multi-Scale Hierarchical Neural Network With Squeeze and Excitation Block for Neonatal Sleep Staging Using a Single-Channel EEG
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DOI：
10.1109/tnsre.2023.3266876
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发表时间：
2023-04
影响因子：
4.9
通讯作者：
Hangyu Zhu;Yan Xu;Ning Shen;Yonglin Wu;Laishuan Wang;Chen Chen-Chen;W. Chen
中科院分区：
文献类型：
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作者：
Hangyu Zhu;Yan Xu;Ning Shen;Yonglin Wu;Laishuan Wang;Chen Chen-Chen;W. Chen
现有的新生儿睡眠分期方法大多采用多脑电通道，以获得较好的效果。然而，它潜在地增加了计算复杂性，并导致数据采集过程中新生儿皮肤损伤的风险增加。本文提出了一种基于单一脑电图通道的多尺度分层神经网络（MS-HNN）与挤压和兴奋（SE）阻滞的新生儿睡眠分期方法。MS-HNN由多尺度卷积神经网络（MSCNN）、时间信息学习（TIL）模块和挤压激励（SE）模块组成。MSCNN可以提取不同尺度和频率的特征，TIL模块可以获取相邻阶段之间的过渡信息。此外，对于这些提取的特征，SE块可以选择性地集中信息特征，削弱冗余特征，从而获得更好的性能。该方法在复旦大学儿童医院64名新生儿的临床数据集上得到了验证。该网络对单脑电通道和八脑电通道新生儿睡眠的三级自动分期准确率分别达到75.4%和76.5%。实验结果表明，该方法在充分利用单信道信息的同时，通过减少信道数量来控制计算量，保持了较好的性能。
Most existing neonatal sleep staging appro- aches applied multiple EEG channels to obtain good performance. However, it potentially increased the computational complexity and led to an increased risk of skin disruption to neonates during data acquisition. In this paper, a multi-scale hierarchical neural network (MS-HNN) with a squeeze and excitation (SE) block for neonatal sleep staging is presented in this study on the basis of a single EEG channel. MS-HNN composes of multi-scale convolutional neural network (MSCNN), temporal information learning (TIL) module, and squeeze and excitation (SE) block. MSCNN can extract features from different scales and frequencies, and TIL module can acquire the transition information among adjacent stages. In addition, for these extracted features, SE block can selectively concentrate on informative features and weaken redundant features for achieving better performance. The proposed approach was validated on a clinical dataset involving 64 neonates from the Children’s Hospital of Fudan University (CHFU). The proposed network achieves an accuracy of 75.4% and 76.5% for three-class automatic neonatal sleep staging with the single-EEG channel and the eight-EEG channels, respectively. The experimental results show that the proposed method can maintain good performance by making full use of the information in the single channel while reducing the channels to control the computational overhead.