Background and objective

Multimodal data analysis and large-scale computational capability is entering medicine in an accelerative fashion and has begun to influence investigational work in a variety of disciplines. It is also informing us of therapeutic interventions that will come about with such development. Epilepsy is a chronic brain disorder in which functional changes may precede structural ones and which may be detectable using existing modalities.

Methods

Functional connectivity analysis using electroencephalography (EEG) and resting state-functional magnetic resonance imaging (rs-fMRI) has provided such meaningful input in cases of epilepsy. By leveraging the potential of autonomic edge computing in epilepsy, we develop and deploy both noninvasive and invasive methods for monitoring, evaluation, and regulation of the epileptic brain. First, an autonomic edge computing framework is proposed for the processing of big data as part of a decision support system for surgical candidacy. Second, a multimodal data analysis using independently acquired EEG and rs-fMRI is presented for estimation and prediction of the epileptogenic network. Third, an unsupervised feature extraction model is developed for EEG analysis and seizure prediction based on a Convolutional deep learning (CNN) structure for distinguishing preictal (pre-seizure) state from non-preictal periods by support vector machine (SVM) classifier.

Results

Experimental and simulation results from actual patient data validate the effectiveness of the proposed methods.

Conclusions

The combination of rs-fMRI and EEG/iEEG can reveal more information about dynamic functional connectivity. However, simultaneous fMRI and EEG data acquisition present challenges. We have proposed system models for leveraging and processing independently acquired fMRI and EEG data.

中文翻译：

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通过深度学习和边缘计算对癫痫脑电图和 rs-fMRI 进行多模态数据分析。

背景和目的

多模态数据分析和大规模计算能力正以加速的方式进入医学领域，并开始影响各种学科的研究工作。它还告诉我们随着这种发展而出现的治疗干预。癫痫是一种慢性脑部疾病，其中功能性变化可能先于结构性变化，并且可以使用现有的方式检测到。

方法

使用脑电图 (EEG) 和静息状态功能磁共振成像 (rs-fMRI) 的功能连接分析为癫痫病例提供了如此有意义的输入。通过利用自主边缘计算在癫痫中的潜力，我们开发和部署了用于监测、评估和调节癫痫大脑的非侵入性和侵入性方法。首先，提出了一种自主边缘计算框架，用于处理大数据，作为手术候选决策支持系统的一部分。其次，提出了使用独立获取的 EEG 和 rs-fMRI 的多模式数据分析，用于估计和预测致痫网络。第三，

结果

来自实际患者数据的实验和模拟结果验证了所提出方法的有效性。

结论

rs-fMRI 和 EEG/iEEG 的结合可以揭示更多关于动态功能连接的信息。然而，同时进行 fMRI 和 EEG 数据采集带来了挑战。我们提出了用于利用和处理独立获取的 fMRI 和 EEG 数据的系统模型。