School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
School of Engineering, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
Comput Methods Programs Biomed. 2024 Feb;244:107992. doi: 10.1016/j.cmpb.2023.107992. Epub 2023 Dec 27.
Sleep staging is an essential step for sleep disorder diagnosis, which is time-intensive and laborious for experts to perform this work manually. Automatic sleep stage classification methods not only alleviate experts from these demanding tasks but also enhance the accuracy and efficiency of the classification process.
A novel multi-channel biosignal-based model constructed by the combination of a 3D convolutional operation and a graph convolutional operation is proposed for the automated sleep stages using various physiological signals. Both the 3D convolution and graph convolution can aggregate information from neighboring brain areas, which helps to learn intrinsic connections from the biosignals. Electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG) and electrocardiogram (ECG) signals are employed to extract time domain and frequency domain features. Subsequently, these signals are input to the 3D convolutional and graph convolutional branches, respectively. The 3D convolution branch can explore the correlations between multi-channel signals and multi-band waves in each channel in the time series, while the graph convolution branch can explore the connections between each channel and each frequency band. In this work, we have developed the proposed multi-channel convolution combined sleep stage classification model (MixSleepNet) using ISRUC datasets (Subgroup 3 and 50 random samples from Subgroup 1).
Based on the first expert's label, our generated MixSleepNet yielded an accuracy, F1-score and Cohen kappa scores of 0.830, 0.821 and 0.782, respectively for ISRUC-S3. It obtained accuracy, F1-score and Cohen kappa scores of 0.812, 0.786, and 0.756, respectively for the ISRUC-S1 dataset. In accordance with the evaluations conducted by the second expert, the comprehensive accuracies, F1-scores, and Cohen kappa coefficients for the ISRUC-S3 and ISRUC-S1 datasets are determined to be 0.837, 0.820, 0.789, and 0.829, 0.791, 0.775, respectively.
The results of the performance metrics by the proposed method are much better than those from all the compared models. Additional experiments were carried out on the ISRUC-S3 sub-dataset to evaluate the contributions of each module towards the classification performance.
睡眠分期是睡眠障碍诊断的一个基本步骤，专家手动进行这项工作既耗时又费力。自动睡眠分期分类方法不仅可以减轻专家的这些繁重任务，还可以提高分类过程的准确性和效率。
我们提出了一种基于多通道生物信号的新型模型，该模型由 3D 卷积运算和图卷积运算相结合构建，用于使用各种生理信号进行自动睡眠分期。3D 卷积和图卷积都可以从相邻脑区聚合信息，这有助于从生物信号中学习内在连接。采用脑电图（EEG）、肌电图（EMG）、眼动电图（EOG）和心电图（ECG）信号提取时域和频域特征。然后，将这些信号分别输入到 3D 卷积和图卷积分支。3D 卷积分支可以探索时间序列中多通道信号和多波段波之间的相关性，而图卷积分支可以探索每个通道和每个频带之间的连接。在这项工作中，我们使用 ISRUC 数据集（子组 3 和子组 1 中的 50 个随机样本）开发了所提出的多通道卷积组合睡眠分期分类模型（MixSleepNet）。
基于第一位专家的标签，我们的 MixSleepNet 生成的 ISRUC-S3 的准确性、F1 分数和 Cohen kappa 分数分别为 0.830、0.821 和 0.782。对于 ISRUC-S1 数据集，它获得的准确性、F1 分数和 Cohen kappa 分数分别为 0.812、0.786 和 0.756。根据第二位专家的评估，ISRUC-S3 和 ISRUC-S1 数据集的综合准确性、F1 分数和 Cohen kappa 系数分别确定为 0.837、0.820、0.789 和 0.829、0.791、0.775。
所提出方法的性能指标结果明显优于所有比较模型的结果。在 ISRUC-S3 子数据集上进行了额外的实验，以评估每个模块对分类性能的贡献。
