Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Sci Rep. 2024 Feb 27;14(1):4797. doi: 10.1038/s41598-024-54727-0.
Sleep research is fundamental to understanding health and well-being, as proper sleep is essential for maintaining optimal physiological function. Here we present SlumberNet, a novel deep learning model based on residual network (ResNet) architecture, designed to classify sleep states in mice using electroencephalogram (EEG) and electromyogram (EMG) signals. Our model was trained and tested on data from mice undergoing baseline sleep, sleep deprivation, and recovery sleep, enabling it to handle a wide range of sleep conditions. Employing k-fold cross-validation and data augmentation techniques, SlumberNet achieved high levels of overall performance (accuracy = 97%; F1 score = 96%) in predicting sleep stages and showed robust performance even with a small and diverse training dataset. Comparison of SlumberNet's performance to manual sleep stage classification revealed a significant reduction in analysis time (~ 50 × faster), without sacrificing accuracy. Our study showcases the potential of deep learning to facilitate sleep research by providing a more efficient, accurate, and scalable method for sleep stage classification. Our work with SlumberNet further demonstrates the power of deep learning in mouse sleep research.
睡眠研究对于理解健康和幸福至关重要，因为适当的睡眠对于维持最佳生理功能至关重要。在这里，我们提出了 SlumberNet，这是一种基于残差网络（ResNet）架构的新型深度学习模型，旨在使用脑电图（EEG）和肌电图（EMG）信号对小鼠的睡眠状态进行分类。我们的模型在进行基础睡眠、睡眠剥夺和恢复睡眠的小鼠数据上进行了训练和测试，使其能够处理广泛的睡眠条件。通过使用 k 折交叉验证和数据增强技术，SlumberNet 在预测睡眠阶段方面实现了很高的整体性能（准确率=97%；F1 得分为 96%），并且即使在较小和多样化的训练数据集上也表现出了强大的性能。将 SlumberNet 的性能与手动睡眠阶段分类进行比较，结果显示分析时间显著减少（约快 50 倍），而不牺牲准确性。我们的研究展示了深度学习在睡眠研究中的潜力，为睡眠阶段分类提供了更高效、准确和可扩展的方法。我们使用 SlumberNet 的工作进一步展示了深度学习在小鼠睡眠研究中的强大功能。
