引入基于区域的池化技术,为深度学习模型处理不同数量的脑电图通道

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Thomas Tveitstøl, Mats Tveter, Ana S. Pérez T., Christoffer Hatlestad-Hall, Anis Yazidi, Hugo L. Hammer, Ira R. J. Hebold Haraldsen
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引用次数: 0

摘要

导言:将人工智能(AI)深度学习(DL)方法应用于新型脑电图(EEG)数据时,面临的一个挑战是 DL 架构无法适应 EEG 通道数量的变化。也就是说,无论是在训练数据中还是在部署时,通道数量都不能变化。在这项工作中,我们提出了一种技术,通过将脑电图蒙太奇分割成不同的区域,并将同一区域内的通道合并到一个区域表示中,来处理这种不同数量的脑电图通道。该解决方案被称为基于区域的池化(RBP)。将蒙太奇分割成区域的过程会在不同的区域配置下反复进行,以尽量减少潜在的信息丢失。由于 RBP 将不同数量的脑电图通道映射到固定数量的区域表示中,因此当前和未来的数字线路架构都可以轻松应用 RBP。为了证明和评估 RBP 是否足以处理不同数量的脑电图通道,我们使用了仅基于脑电图的性分类作为测试示例。DL 模型在 129 个通道上进行了训练,并使用相同的通道位置方案在 32、65 和 129 个通道版本的数据上进行了测试。比较基准是对缺失通道进行零填充和应用球形样条插值。结果对于 32 通道系统版本,各次折叠的平均 AUC 值分别为:RBP(93.34%)、RBP(93.34%)和 RBP(93.34%):RBP(93.34%)、球形样条插值(93.36%)和零填充(76.82%)。同样,65 通道系统版本的性能表现为RBP(93.66%)、球面样条插值(93.50%)和零填充(85.58%)。最后,129 通道系统版本的结果如下:结论总之,RBP 得到了与球面样条插值相似的结果,而零填充的结果则优于球面样条插值。我们鼓励在跨数据集设置中进一步研究和开发 DL 模型,包括使用 RBP 和球面样条插值等方法来处理不同数量的脑电图通道。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models
Introduction

A challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture's lack of adaptability to changing numbers of EEG channels. That is, the number of channels cannot vary neither in the training data, nor upon deployment. Such highly specific hardware constraints put major limitations on the clinical usability and scalability of the DL models.

Methods

In this work, we propose a technique for handling such varied numbers of EEG channels by splitting the EEG montages into distinct regions and merge the channels within the same region to a region representation. The solution is termed Region Based Pooling (RBP). The procedure of splitting the montage into regions is performed repeatedly with different region configurations, to minimize potential loss of information. As RBP maps a varied number of EEG channels to a fixed number of region representations, both current and future DL architectures may apply RBP with ease. To demonstrate and evaluate the adequacy of RBP to handle a varied number of EEG channels, sex classification based solely on EEG was used as a test example. The DL models were trained on 129 channels, and tested on 32, 65, and 129-channels versions of the data using the same channel positions scheme. The baselines for comparison were zero-filling the missing channels and applying spherical spline interpolation. The performances were estimated using 5-fold cross validation.

Results

For the 32-channel system version, the mean AUC values across the folds were: RBP (93.34%), spherical spline interpolation (93.36%), and zero-filling (76.82%). Similarly, on the 65-channel system version, the performances were: RBP (93.66%), spherical spline interpolation (93.50%), and zero-filling (85.58%). Finally, the 129-channel system version produced the following results: RBP (94.68%), spherical spline interpolation (93.86%), and zero-filling (91.92%).

Conclusion

In conclusion, RBP obtained similar results to spherical spline interpolation, and superior results to zero-filling. We encourage further research and development of DL models in the cross-dataset setting, including the use of methods such as RBP and spherical spline interpolation to handle a varied number of EEG channels.

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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
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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