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首页> 外文会议>2019 Spring Simulation Conference >A Machine Learning Model to Predict Seizure Susceptibility from Resting-State fMRI Connectivity
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A Machine Learning Model to Predict Seizure Susceptibility from Resting-State fMRI Connectivity

机译：通过静息状态fMRI连通性预测癫痫发作易感性的机器学习模型

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摘要

Traumatic brain injury (TBI) is a leading cause of disability globally. Many patients develop post-traumatic epilepsy, or recurrent seizures following TBI. In recent years, significant efforts have been made to identify biomarkers of epileptogenesis that may assist in preventing seizure occurrence by identifying high-risk patients. We present a novel method of assessing seizure susceptibility using data from 49 patients enrolled in the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). We employ a machine learning paradigm that utilizes a Random Forest classifier trained with resting-state functional magnetic resonance imaging (fMRI) data to predict seizure outcomes. Following 100 rounds of stratified cross-validation with 70% of resting state fMRI scans as the training set and 30% as the testing set, our model was found to assess seizure outcome in the testing set with 69% accuracy. To validate the method, we compared our results with classification by Support Vector Machines and Neural Network classifiers.
机译：颅脑外伤（TBI）是全球残疾的主要原因。许多患者在TBI后发生创伤后癫痫或反复发作​​。近年来，人们做出了巨大的努力来识别癫痫发生的生物标志物，这些标志物可以通过识别高危患者来帮助预防癫痫发作的发生。我们提出了一种新的方法来评估癫痫发作易感性，使用来自参与抗癫痫治疗（EpiBioS4Rx）的癫痫生物信息学研究的49位患者的数据。我们采用了一种机器学习范例，该范例利用随机森林分类器进行训练，并利用静止状态功能磁共振成像（fMRI）数据预测癫痫发作的结果。经过100轮分层交叉验证，其中70％的静息状态fMRI扫描作为训练集，30％作为测试集，我们的模型被发现以69％的准确性评估测试集中的癫痫发作结果。为了验证该方法，我们将结果与支持向量机和神经网络分类器的分类进行了比较。

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  • 会议地点 Tucson(US)
  • 作者

    Rachael Garner; Marianna La Rocca; Giuseppe Barisano; Arthur W. Toga; Dominique Duncan; Paul Vespa;

  • 作者单位

    Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Zonal Avenue Los Angeles, CA, 2025, USA;

    Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Zonal Avenue Los Angeles, CA, 2025, USA;

    Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Zonal Avenue Los Angeles, CA, 2025, USA;

    Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Zonal Avenue Los Angeles, CA, 2025, USA;

    Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Zonal Avenue Los Angeles, CA, 2025, USA;

    Department of Neurosurgery, University of California, Los Angeles 300 Stein Plaza, Suite, Los Angeles, CA, 420, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Functional magnetic resonance imaging; Injuries; Machine learning; Radio frequency; Support vector machines; Epilepsy; Training;

    机译：功能磁共振成像;损伤;机器学习;射频;支持向量机;癫痫;训练;;

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