Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning
Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning
复制标题
使用深度学习的长期头皮脑电图记录的专家级自动睡眠分期
DOI：
10.1093/sleep/zsaa112
复制
发表时间：
2020-11-01
期刊：
影响因子：
5.6
通讯作者：
Lam, Alice D.
中科院分区：
文献类型：
--
作者：
Abou Jaoude, Maurice;Sun, Haoqi;Lam, Alice D.
研究目的：开发一种高性能的自动化睡眠评分算法，可应用于长期头皮脑电图（EEG）记录。方法：使用来自6，431名患者的多导睡眠图临床数据集（MGH-PSG数据集），我们训练了一个深度神经网络，根据头皮EEG数据对睡眠阶段进行分类。该算法由用于特征提取的卷积神经网络和用于提取睡眠阶段时间依赖性的循环神经网络组成。该算法的输入是四个头皮EEG双极通道（F3-C3、C3-O 1、F4-C4和C4-O2），可以从任何标准PSG或头皮EEG记录中导出。我们最初在MGH-PSG数据集上训练该算法，并使用迁移学习在112名患者的长期（24-72小时）头皮EEG记录数据集（scalpEEG dataset）上对其进行微调。结果：该算法在MGH-PSG保持测试集上实现了0.74的Cohen's kappa，并在scalpEEG数据集上优化后交叉验证了0.78的Cohen's kappa。该算法在两个公开的PSG数据集上也表现良好，表现出很高的泛化能力。所有数据集上的性能与人类睡眠分期专家的评分员间一致性相当（Cohen's kappa类似于0.75 +/- 0.11）。该算法在长期头皮EEG上的性能在很宽的年龄范围内和常见的EEG背景abnormality.Conclusion：我们开发了一种深度学习算法，可以在长期头皮EEG记录上实现人类专家级的睡眠分期性能。该算法，我们已经公开提供，极大地促进了睡眠相关研究的大型长期EEG临床数据集的使用。
Study Objectives: Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings.Methods: Using a clinical dataset of polysomnograms from 6,431 patients (MGH-PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm's inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH-PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24-72 h) scalp EEG recordings from 112 patients (scalpEEG dataset).Results: The algorithm achieved a Cohen's kappa of 0.74 on the MGH-PSG holdout testing set and cross-validated Cohen's kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen's kappa similar to 0.75 +/- 0.11). The algorithm's performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities.Conclusion: We developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.