...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >A Data-Driven Approach to Predict and Classify Epileptic Seizures from Brain-Wide Calcium Imaging Video Data
【24h】

A Data-Driven Approach to Predict and Classify Epileptic Seizures from Brain-Wide Calcium Imaging Video Data

机译：一种数据驱动方法来预测和分类脑级钙成像视频数据的癫痫癫痫发作方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The prediction of epileptic seizures has been an essential problem of epilepsy study. The calcium imaging video data images the whole brain-wide neurons activities with electrical discharge recorded by calcium fluorescence intensity (CFI). In this paper, using the zebrafish's brain-wide calcium image video data, we propose a data-driven approach to effectively detect the systemic change-point, and further predict the epileptic seizures. Our approach includes two phases: offline training and online testing. Specifically, during offline training, we extract features and confirm the existence of systemic change-point, then estimate the ratio of unchanged system duration to interictal period duration. For online testing, we implement a statistical model to estimate the change-point, and then predict the onset of epileptic seizure. The testing results show that our proposed approach could effectively predict the time range of future epileptic seizure. Furthermore, we explore the macroscopic patterns of epileptic and control cases, and extract features based on the pattern difference, then implement and compare the classification performance from four machine learning models. Based on the data structure, we also propose a new method to discretize related features, and combine with hierarchical clustering to better visualize and explain the pattern difference between epileptic and control cases.
机译：癫痫癫痫发作的预测是癫痫研究的重要问题。钙成像视频数据通过钙荧光强度（CFI）记录的电气放电进行整个脑宽的神经元活动。在本文中，使用Zebrafish的脑宽钙图像视频数据，我们提出了一种数据驱动方法来有效地检测到系统变化点，并进一步预测癫痫发作。我们的方法包括两个阶段：离线培训和在线测试。具体而言，在离线培训期间，我们提取特征并确认系统变化点的存在，然后估计未变的系统持续时间与内部持续时间的比率。为了在线测试，我们实施统计模型来估计变化点，然后预测癫痫发作的开始。测试结果表明，我们的建议方法可以有效预测未来癫痫发作的时间范围。此外，我们探索癫痫和控制案例的宏观模式，并基于模式差异提取特征，然后实现并比较来自四台机器学习模型的分类性能。基于数据结构，我们还提出了一种新方法来离散相关特征，并与分层聚类结合以更好地可视化并解释癫痫和控制案件之间的模式差异。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号