Dimulescu Cristiana, Donle Leonhard, Cakan Caglar, Goerttler Thomas, Khakimova Lilia, Ladenbauer Julia, Flöel Agnes, Obermayer Klaus
Department of Software Engineering and Theoretical Computer Science, Technical University Berlin, Berlin, Germany.
Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.
Front Neuroinform. 2024 Feb 5;18:1338886. doi: 10.3389/fninf.2024.1338886. eCollection 2024.
We aimed to build a tool which facilitates manual labeling of sleep slow oscillations (SOs) and evaluate the performance of traditional sleep SO detection algorithms on such a manually labeled data set. We sought to develop improved methods for SO detection.
SOs in polysomnographic recordings acquired during nap time from ten older adults were manually labeled using a custom built graphical user interface tool. Three automatic SO detection algorithms previously used in the literature were evaluated on this data set. Additional machine learning and deep learning algorithms were trained on the manually labeled data set.
Our custom built tool significantly decreased the time needed for manual labeling, allowing us to manually inspect 96,277 potential SO events. The three automatic SO detection algorithms showed relatively low accuracy (max. 61.08%), but results were qualitatively similar, with SO density and amplitude increasing with sleep depth. The machine learning and deep learning algorithms showed higher accuracy (best: 99.20%) while maintaining a low prediction time.
Accurate detection of SO events is important for investigating their role in memory consolidation. In this context, our tool and proposed methods can provide significant help in identifying these events.
我们旨在构建一种工具，以方便对睡眠慢波振荡（SOs）进行手动标注，并评估传统睡眠SO检测算法在这样一个手动标注数据集上的性能。我们试图开发改进的SO检测方法。
使用定制的图形用户界面工具，对10名老年人午睡期间采集的多导睡眠图记录中的SOs进行手动标注。在此数据集上评估了文献中先前使用的三种自动SO检测算法。在手动标注的数据集上训练了额外的机器学习和深度学习算法。
我们定制的工具显著减少了手动标注所需的时间，使我们能够手动检查96277个潜在的SO事件。三种自动SO检测算法的准确率相对较低（最高61.08%），但结果在定性上相似，SO密度和幅度随睡眠深度增加。机器学习和深度学习算法显示出更高的准确率（最佳：99.20%），同时保持较低的预测时间。
准确检测SO事件对于研究它们在记忆巩固中的作用很重要。在这种情况下，我们的工具和提出的方法可以在识别这些事件方面提供显著帮助。
