• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>IEEE International Symposium on Computer-Based Medical Systems >Automatic Detection of Tonic-Clonic and Myoclonic Epileptic Seizures Using Prefrontal Electroencephalography (EEG)
【24h】

Automatic Detection of Tonic-Clonic and Myoclonic Epileptic Seizures Using Prefrontal Electroencephalography (EEG)

机译：使用前额叶脑电图（EEG）自动检测滋补克隆和肌阵挛性癫痫发作

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 a neurological disease that affects about 50 million people worldwide. It is a disorder of the central nervous system, characterized by recurrent seizures that can have a massive impact in the physical and mental health of the people who suffer from it, as well as their loved ones. Long-term monitoring of epilepsy in uncontrolled environments is key to provide accurate characterization of the disease, and to create tools that improve the patients' lives. Although some wearable devices (particularly with motion and cardiac-based sensors) are quickly gaining ground as everyday use monitoring devices, in the scope of epilepsy, electroencephalography (EEG) remains the gold-standard. Therefore, it is of utmost importance to include this modality in ambulatory settings, leveraging its extensive presence in literature and available databases. Nevertheless, in long-term recordings, having information about the onset of the seizure is of utmost importance for effective analysis of the collected data. Hence, this work explores the use of a single-channel, nonintrusive, EEG configuration in automatic seizure detection, with the purpose of event annotation in long-term recordings. This is a key element to the creation of multimodal datasets that can be used in seizure detection and, eventually, prediction, as well as towards comprehensive multimodal epilepsy monitoring techniques. A seizure-specific Support Vector Machines (SVM) classifier was designed for labeling eight different types of seizure, using a limited-channel configuration (Fp1-Fp2). Our work uses the TUH EEG Seizure Corpus, for which encouraging results were achieved for tonic-clonic and myoclonic seizures, with sensitivities of 98.9% and 98.2%, as well as precisions of 100% and 99.8%, respectively.
机译：癫痫是一种神经疾病，影响全世界约有5000万人。它是中枢神经系统的疾病，其特征在于经常发作的癫痫发作，这可能对患有它的人的身心健康以及他们所爱的人来说。在不受控制的环境中对癫痫的长期监测是提供准确表征疾病的关键，并创建改善患者生命的工具。尽管一些可穿戴设备（特别是运动和基于动作和基于动作和心脏的传感器）很快地获得接地，但在癫痫的范围内，脑电图（EEG）仍然是金标准。因此，在动态设置中包含此模型至关重要，可以利用其在文献和可用数据库中的广泛存在。然而，在长期记录中，具有关于癫痫发作的信息的信息对于有效分析所收集的数据至关重要。因此，这项工作探讨了在自动癫痫发作检测中使用单通道，非流程，EEG配置，具有长期记录中的事件注释的目的。这是创建可以用于癫痫发作检测和最终预测的多模式数据集的关键元素，以及朝向综合多模式癫痫监测技术。使用有限通道配置（FP1-FP2），设计了特定于特定的支持向量机（SVM）分类器（SVM）分类器用于标记八种不同类型的癫痫发作。我们的作品使用Tuh EEG癫痫发作语料库，为滋补克隆和肌阵挛性癫痫发作，敏感性令人鼓舞的结果，敏感性分别为98.9％和98.2％，以及100％和99.8％的精确。

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