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Nonlinear Methods for Detection and Prediction of Epileptic Seizures

机译：检测和预测癫痫发作的非线性方法

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摘要

A reliable method of seizure detection and prediction in people with epilepsy would provide a key strategy for assessing risk of sudden unexpected death in epilepsy (SUDEP) in those that suffer from uncontrollable seizures, and would guide research in the development of preventative interventions. This research proposes objective indications of seizure onset observed from electroencephalogram (EEG). The algorithms utilize scalp EEG that is minimally invasive and has shown promise for high sensitivity and specificity in seizure event forewarning. This approach considers the brain as a nonlinear dynamical system whose state can be derived through time delay embedding of the time-serial EEG data and characterized to determine change in brain dynamics related to the preictal state. This method targets specific phase-space graph properties as biomarkers for seizure detection and prediction. The data analysis approach efficiently processes individual windows of data, correlates historical degrees of change in the brain state from repeated measurements, and makes accurate forewarning of seizure onset. Specifically, we contribute to the field in the following ways:;Seizure Prediction. We provide three novel techniques for predicting seizures prior to onset: 1. Phase-Space Adjacency Spectrum: This method targets the spectrum of phase-space graph adjacency matrices as a biomarker for seizure prediction. The best results corresponded to an accuracy of 97% (58/60), a sensitivity of 100% (40/40), and a specificity of 90% (18/20) on training data. In out of sample testing, this method achieved an accuracy of 75%, specificity of 70% (7/10), and sensitivity of 80% (8/10). After minor adjustments to a single parameter, the out of sample test achieved an overall accuracy of 90% (18/20). 2. Phase-Space Laplacian Spectrum: This method targets the spectrum of phase-space graph laplacian matrices as a biomarker for seizure prediction. The best results corresponded to an accuracy of 93% (56/60), a sensitivity of 93% (37/40), and a specificity of 95% (19/20). Out of sample testing resulted in a specificity of 60% (6/10) and sensitivity of 70% (7/10). After minor adjustments to a single parameter, the out of sample test achieved an overall accuracy of 80%. 3. Hypergraph Analysis of Phase-Space Graphs: This method analyzes subsets of the edge set of phase-space graphs identified as hyperedges of a hypergraph as biomarkers for seizure prediction. The hypergraph is represented with a matrix which captures the essential structure and connectivity of the graph. The spectral features of the triangle matrix are used to predict seizure onset. This method yields a training accuracy of 93% and testing accuracy of 80%.;Seizure Detection. We provide a novel technique for a patient specific seizure detection algorithm. This method combines phase-space analysis and deep learning using convolutional neural networks (CNN) to indicate seizure onset. The output of the CNN is filtered using a combination of exponential time decay and a sliding mean window. This method achieved a 100% true positive and 99% true negative rate on four patients.
机译：癫痫患者癫痫发作的可靠检测和预测方法将为评估癫痫发作不可控制的癫痫患者突然意外死亡的风险提供一种关键策略，并将指导预防性干预措施的研究。这项研究提出了从脑电图（EEG）观察到的癫痫发作的客观指征。该算法利用微创的头皮脑电图，在癫痫发作预警中表现出很高的敏感性和特异性。这种方法将大脑视为非线性动力学系统，其状态可以通过时间序列EEG数据的时间延迟嵌入来推导，并且其特征在于确定与发作前状态有关的大脑动力学变化。该方法针对特定的相空间图属性作为癫痫发作检测和预测的生物标记。数据分析方法有效地处理了各个数据窗口，将重复测量的大脑状态的历史变化程度关联起来，并对癫痫发作进行了准确的预警。具体来说，我们通过以下方式为该领域做出贡献：癫痫发作预测。我们提供了三种在发作前预测癫痫发作的新技术：1.相空间邻接光谱：该方法的目标是相空间图邻接矩阵的光谱，作为癫痫发作预测的生物标记。最佳结果对应于训练数据的97％（58/60）的准确度，100％（40/40）的敏感性和90％（18/20）的特异性。在样品外测试中，该方法的准确度为75％，特异性为70％（7/10），灵敏度为80％（8/10）。对单个参数进行细微调整后，样本外测试的总体准确度达到90％（18/20）。 2.相空间拉普拉斯谱：该方法的目标是相空间图拉普拉斯矩阵的谱，作为癫痫发作预测的生物标记。最好的结果对应于93％（56/60）的准确度，93％（37/40）的灵敏度和95％（19/20）的特异性。样本外测试的特异性为60％（6/10），灵敏度为70％（7/10）。对单个参数进行细微调整后，样品外测试的总体精度达到80％。 3.相空间图的超图分析：此方法分析相空间图的边缘集的子集，这些边缘集被识别为超图的超边缘作为癫痫发作预测的生物标记。超图用一个矩阵表示，该矩阵捕获图的基本结构和连通性。三角矩阵的光谱特征用于预测癫痫发作。这种方法的训练精度为93％，测试精度为80％。我们为患者特定的癫痫发作检测算法提供了一种新颖的技术。该方法结合了相空间分析和使用卷积神经网络（CNN）的深度学习来指示癫痫发作。 CNN的输出使用指数时间衰减和滑动平均窗口的组合进行滤波。该方法对四名患者实现了100％的真实阳性和99％的真实阴性率。

著录项

  • 作者

    Luckett, Patrick H.;

  • 作者单位

    University of South Alabama.;

  • 授予单位 University of South Alabama.;
  • 学科 Computer science.;Neurosciences.;Artificial intelligence.
  • 学位 Ph.D.
  • 年度 2018
  • 页码 149 p.
  • 总页数 149
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 药物化学;
  • 关键词

  • 入库时间 2022-08-17 11:38:58

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