• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Tools with Artificial Intelligence >A New Approach to Automated Epileptic Diagnosis Using EEG and Probabilistic Neural Network
【24h】

A New Approach to Automated Epileptic Diagnosis Using EEG and Probabilistic Neural Network

机译：脑电图和概率神经网络自动癫痫诊断的新方法

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Epilepsy is one of the most common neurological disorders that greatly impair patients' daily lives. Traditional epileptic diagnosis relies on tedious visual screening by neurologists from lengthy EEG recording that requires the presence of seizure (ictal) activities. Nowadays, there are many systems helping the neurologists to quickly find interesting segments from the lengthy signal by automatic seizure detection. However, we notice that it is very difficult, if not impossible, to obtain long-term EEG data with seizure activities for epilepsy patients in areas lack of medical resources and trained neurologists. Therefore, we propose to study automated epileptic diagnosis using interictal EEG data that is much easier to collect than ictal data. The authors are not aware of any report on automated EEG diagnostic system that can accurately distinguish patients' interictal EEG from the EEG of normal people. The research presented in this paper, therefore, aims to develop an automated diagnostic system that can use interictal EEG data to diagnose whether the person is epileptic. Such a system should also detect seizure activities for further investigation by doctors and potential patient monitoring. To develop such a system, we extract three classes of features from the EEG data and build a Probabilistic Neural Network (PNN) fed with these features. Leave-one-out cross-validation (LOO-CV) on a widely used epileptic-normal data set reflects an impressive 99.3% accuracy of our system on distinguishing normal people's EEG from patients' interictal EEG. We also find our system can be used in patient monitoring (seizure detection) and seizure focus localization, with 96.7% and 76.5% accuracy respectively on the data set.
机译：癫痫是最常见的神经系统疾病之一，大大损害患者的日常生活。传统的癫痫诊断依赖于神经根学家从冗长的脑电图录制令人疑望的视觉筛查，这需要存在癫痫发作（ictal）活动。如今，有许多系统帮助神经病学家通过自动癫痫发作检测快速找到与冗长信号中有趣的段。然而，我们注意到，如果不是不可能的话，非常困难，以获得癫痫患者缺乏医疗资源和培训的神经科学家的癫痫患者的长期脑电图数据。因此，我们建议使用更容易收集的嵌入脑电图数据来研究自动癫痫诊断，而不是ICTAL数据。作者不了解关于自动脑电图诊断系统的任何报告，可以准确地区分患者的患者的闭合脑电图。因此，本文提出的研究旨在开发一种自动诊断系统，可以使用Interictal EEG数据来诊断该人是否是癫痫症。这种制度还应检测癫痫发作活动，供医生进一步调查和潜在的患者监测。为了开发这样的系统，我们从脑电图数据中提取三类功能，并构建具有这些功能的概率神经网络（PNN）。广泛使用的癫痫型正常数据集的休留交叉验证（LOO-CV）反映了我们在从患者闭规性脑电图中区分正常人的脑电图的系统令人印象深刻的99.3％的准确性。我们还发现我们的系统可用于患者监测（癫痫发作检测）和扣押聚焦本地化，分别在数据集中分别进行96.7％和76.5％的准确性。

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