一种基于脑电和脑电的癫痫自动诊断新方法 概率神经网络

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摘要翻译：
癫痫是最常见的神经系统疾病之一，严重影响患者的日常生活。传统的癫痫诊断依赖于神经科医生从冗长的脑电图记录中进行繁琐的视觉筛选，这需要癫痫活动的存在。现在，有许多系统帮助神经科医生通过自动检测癫痫发作来快速发现冗长信号中感兴趣的片段。然而，我们注意到，在缺乏医疗资源和训练有素的神经科医生的地区，要获得癫痫患者癫痫发作活动的长期EEG数据是非常困难的，如果不是不可能的话。因此，我们建议利用比发作数据更容易收集的发作间期脑电图数据来研究癫痫的自动诊断。作者不知道任何关于自动脑电诊断系统的报告，可以准确地区分病人的发作间期脑电和正常人的脑电。因此，本文的研究旨在开发一个自动诊断系统，可以利用发作间期脑电数据来诊断患者是否为癫痫患者。这样的系统还应该检测癫痫发作活动，以便医生进一步调查和潜在的患者监测。为了开发这样一个系统，我们从脑电数据中提取了四类特征，并用这些特征构建了概率神经网络(PNN)。在一个广泛使用的癫痫正常数据集上的留一交叉验证（LOO-CV）反映了我们的系统在区分正常人和患者发作间期脑电方面的99.5%的准确率。我们还发现我们的系统可以用于病人监测（癫痫检测）和癫痫病灶定位，在数据集上的准确率分别为96.7%和77.5%。
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英文标题：
《A New Approach to Automated Epileptic Diagnosis Using EEG and
  Probabilistic Neural Network》
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作者：
Forrest Sheng Bao, Donald Yu-Chun Lie, Yuanlin Zhang
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  Epilepsy is one of the most common neurological disorders that greatly impair patient' 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 of 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 four 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 99.5% accuracy of our system on distinguishing normal people's EEG from patient's interictal EEG. We also find our system can be used in patient monitoring (seizure detection) and seizure focus localization, with 96.7% and 77.5% accuracy respectively on the data set.
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PDF链接：
https://arxiv.org/pdf/0804.3361

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关键词：神经网络 新方法 神经网 localization respectively 诊断 医生 epileptic 脑电 automated