Cristiana Dimulescu, Leonhard Donle, Caglar Cakan, Thomas Goerttler, Lilia Khakimova, Julia Ladenbauer, Agnes Flöel, Klaus Obermayer
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引用次数: 0
Abstract
Study objectives
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.
Method
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.
Results
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.
Conclusions
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.
研究目的我们旨在开发一种便于手动标注睡眠慢振荡（SO）的工具，并评估传统睡眠SO检测算法在这种手动标注数据集上的性能。我们试图开发出更好的SO检测方法。方法使用定制的图形用户界面工具手动标注十位老年人午睡时获得的多导睡眠图记录中的SO。在此数据集上评估了之前在文献中使用的三种自动 SO 检测算法。结果我们的定制工具大大减少了人工标注所需的时间，使我们能够人工检测 96,277 个潜在的 SO 事件。三种自动 SO 检测算法的准确率相对较低（最高为 61.08%），但结果在本质上是相似的，SO 密度和振幅随着睡眠深度的增加而增加。机器学习和深度学习算法显示出更高的准确率（最佳：99.20%），同时保持了较低的预测时间。在这种情况下，我们的工具和建议的方法可以为识别这些事件提供重要帮助。
期刊介绍：
Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide.
Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states.
Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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