基于脑电多视图混合神经网络的时空半监督睡眠分期

睡眠分期是评价睡眠质量的必要基础，现阶段的工作大部分采用全监督学习和单一维度视图信息进行，这不仅需要技师进行大量的睡眠数据标注，还可能因特征提取不充分而导致分期准确率受限的问题。利用半监督学习策略，实现对脑电无标注数据的学习。提出一种多视图混合神经网络，首先用多通道视图时频域机制分别提取时域信号特征和空域信号特征，实现多视图特征提取；再通过注意力机制加强对显著性特征的提取；最后将上述混合特征融合并分类。在三个公开数据集和一个私有数据集中与全监督学习进行了对比评估，半监督学习取得平均准确率为81.0%，卡帕值为73.2%。结果表明，本文模型可以与全监督学习的睡眠分期模型相媲美，同时显著减少技师标注数据的工作量。 

Multi-view hybrid neural network for spatiotemporal semi-supervised sleep staging

Sleep takes approximately 1/3 of a person’s lifetime; therefore, its quality profoundly affects learning, physical recovery, and metabolism. Clinically relevant human physiological data are collected using polysomnography, which is analyzed by sleep technologists to determine sleep stages. However, the manual method is prone to having a cumbersome workload due to a large amount of data analysis and different data formats. Simultaneously, manually analyzed results are influenced by doctors’ medical clinical experience, which may cause inconsistent diagnoses. Recently, with the development of artificial intelligence, computer science, other technologies, and their interdisciplinarity, a series of typical achievements have been accomplished in intelligent diagnosis, laying the foundation for medical artificial intelligence in the sleep medicine field. In sleep research, realizing automatic sleep signal analysis and recognition assists doctors in diagnosis and reduces their workload, thus having important clinical significance and application value. Although deep neural networks are becoming popular for automatic sleep stage classification with supervised learning, large-scale, labeled datasets remain difficult to acquire. Learning from raw polysomnography signals and derived time-frequency image representations has been an interesting solution. However, extracting features from only a single dimension leads to inadequate feature extraction and, thus, limited accuracy. Hence, this paper aims to learn multi-view representations for physiological signals with semi-supervised learning. Specifically, we make the following contributions: (1) We propose a multi-view, hybrid neural network model containing a multichannel view time-frequency domain feature extraction mechanism, an attention mechanism, and a feature fusion module. Among these aspects, the multichannel view time-frequency domain mechanism extracts time domain and frequency domain signal features to achieve multi-view feature extraction. The attention mechanism module enhances salience features and achieves interclass feature extraction in the frequency domain. The feature fusion module fuses and classifies the above features. (2) A semi-supervised learning strategy is used to learn unlabeled electroencephalogram (EEG) data, which solves the problem of sleep data underutilization due to insufficient labeling of EEG signals in clinical practice. (3) Extensive experiments conducted on sleep stage classification demonstrate state-of-the-art performance compared with supervised learning and a semi-supervised baseline. Experimental results on three public databases (Sleep?EDF, DOD?H, and DOD?O) and one private database show that our semi-supervised method achieves accuracies of 81.6%, 81.5%, 79.2%, and 75.4%. The results show that our proposed model is comparable to a fully supervised sleep staging model while substantially reducing the technician’s workload in data labeling.