基于脑电图的跨受试者癫痫发作预测的领域适配

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Imene Jemal, Lina Abou-Abbas, Khadidja Henni, Amar Mitiche, Neila Mezghani
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引用次数: 0

摘要

预测癫痫发作的能力是防止患者受伤和出现健康并发症的保障。然而,癫痫发作预测的一大挑战来自于患者数据的巨大变异性。普通的患者特异性方法适用于每个独立的患者,但由于数据的可变性,对其他患者的效果往往不佳。本研究的目的是提出能够处理这种可变性并在不同患者之间泛化的深度学习模型。本研究通过引入新型跨受试者和多受试者预测模型来应对这一挑战。多受试者模型拓宽了患者特定模型的范围,以考虑来自专门的患者集合的数据,从而提供一些有用的(尽管相对适度)泛化水平。该模型的基本神经网络架构随后被调整为跨受试者预测,从而提供了更广泛、更现实的应用范围。对于累积性能和泛化能力而言,跨主体建模通过领域适应性得到了增强。使用公开的 CHB-MIT 和 SIENA 数据集进行的实验评估表明,与现有作品相比,我们的多主体模型取得了更好的性能。然而,跨主体在应用于不同患者时面临挑战。最后,通过研究三种领域适应方法,CHB-MIT 和 SIENA 数据集的模型准确率分别显著提高了 10.30% 和 7.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Domain adaptation for EEG-based, cross-subject epileptic seizure prediction
The ability to predict the occurrence of an epileptic seizure is a safeguard against patient injury and health complications. However, a major challenge in seizure prediction arises from the significant variability observed in patient data. Common patient-specific approaches, which apply to each patient independently, often perform poorly for other patients due to the data variability. The aim of this study is to propose deep learning models which can handle this variability and generalize across various patients. This study addresses this challenge by introducing a novel cross-subject and multi-subject prediction models. Multiple-subject modeling broadens the scope of patient-specific modeling to account for the data from a dedicated ensemble of patients, thereby providing some useful, though relatively modest, level of generalization. The basic neural network architecture of this model is then adapted to cross-subject prediction, thereby providing a broader, more realistic, context of application. For accrued performance, and generalization ability, cross-subject modeling is enhanced by domain adaptation. Experimental evaluation using the publicly available CHB-MIT and SIENA data datasets shows that our multiple-subject model achieved better performance compared to existing works. However, the cross-subject faces challenges when applied to different patients. Finally, through investigating three domain adaptation methods, the model accuracy has been notably improved by 10.30% and 7.4% for the CHB-MIT and SIENA datasets, respectively.
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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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