基于深度学习的脑电图时频图像视觉复杂性分析:它能定位大脑中的致痫区吗?

IF 1.8 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Algorithms Pub Date : 2023-12-15 DOI:10.3390/a16120567
N. Makaram, Sarvagya Gupta, M. Pesce, J. Bolton, Scellig Stone, Daniel Haehn, Marc Pomplun, Christos Papadelis, Phillip L Pearl, Alexander Rotenberg, P. E. Grant, Eleonora Tamilia
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引用次数: 0

摘要

在耐药性癫痫患者中,通常需要通过目测颅内脑电图(iEEG)信号来定位致痫区(EZ)并指导神经外科手术。对 iEEG 时频(TF)图像进行视觉评估是信号检查的一种替代方法,但细微的变化可能会逃过人眼的眼睛。在此,我们提出了一种基于深度学习的视觉复杂性度量来解释从 iEEG 数据中提取的 TF 图像,并旨在评估其识别大脑中 EZ 的能力。我们分析了来自 20 名耐药性癫痫患儿的 1928 个发作间期 iEEG 数据,这些患儿在接受神经外科手术后癫痫不再发作。我们在磁共振成像中定位了每个 iEEG 接触点,为每个接触点创建了 TF 图像(1-70 Hz),并使用预先训练好的 VGG16 网络,通过从 13 个卷积层中提取无监督激活能量 (UAE) 来测量其视觉复杂性。我们通过特定于患者和层的阈值(基于极值分布)并使用支持向量机分类器,利用 UAE 值确定大脑中的兴趣点。结果显示,癫痫发作区内的触点比发作区外的触点显示出更低的 UAE 值,深层(L10、L12 和 L13:p < 0.001)的差异更大。此外,使用支持向量机识别的兴趣点对 EZ 的定位精度为 7 毫米。总之,我们提出了一种手术前计算机化工具,它有助于在患者的核磁共振成像中进行 EZ 定位,而无需进行长期的 iEEG 检查。
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Deep Learning-Based Visual Complexity Analysis of Electroencephalography Time-Frequency Images: Can It Localize the Epileptogenic Zone in the Brain?
In drug-resistant epilepsy, a visual inspection of intracranial electroencephalography (iEEG) signals is often needed to localize the epileptogenic zone (EZ) and guide neurosurgery. The visual assessment of iEEG time-frequency (TF) images is an alternative to signal inspection, but subtle variations may escape the human eye. Here, we propose a deep learning-based metric of visual complexity to interpret TF images extracted from iEEG data and aim to assess its ability to identify the EZ in the brain. We analyzed interictal iEEG data from 1928 contacts recorded from 20 children with drug-resistant epilepsy who became seizure-free after neurosurgery. We localized each iEEG contact in the MRI, created TF images (1–70 Hz) for each contact, and used a pre-trained VGG16 network to measure their visual complexity by extracting unsupervised activation energy (UAE) from 13 convolutional layers. We identified points of interest in the brain using the UAE values via patient- and layer-specific thresholds (based on extreme value distribution) and using a support vector machine classifier. Results show that contacts inside the seizure onset zone exhibit lower UAE than outside, with larger differences in deep layers (L10, L12, and L13: p < 0.001). Furthermore, the points of interest identified using the support vector machine, localized the EZ with 7 mm accuracy. In conclusion, we presented a pre-surgical computerized tool that facilitates the EZ localization in the patient’s MRI without requiring long-term iEEG inspection.
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来源期刊
Algorithms
Algorithms Mathematics-Numerical Analysis
CiteScore
4.10
自引率
4.30%
发文量
394
审稿时长
11 weeks
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