摘要翻译：
全世界大约有5000多万人患有癫痫。传统的癫痫诊断依赖于由训练有素的临床医生从包含癫痫（ictal）活动存在的冗长脑电图记录中进行繁琐的视觉筛选。现在有很多自动系统可以识别癫痫相关的脑电信号来帮助诊断。然而，获取具有癫痫发作活动的长期脑电数据非常昂贵和不便，尤其是在医疗资源短缺的地区。我们在这篇文章中证明了我们可以利用头皮发作间期的脑电数据来自动诊断一个人是否是癫痫，这比发作期的数据更容易收集。在我们的脑电自动识别系统中，我们从脑电数据中提取三类特征，并用这些特征建立概率神经网络。我们优化了特征提取参数，并通过投票机制将这些PNN组合起来。结果表明，我们的系统达到了令人印象深刻的94.07%的正确率，与有经验的医学专家所报道的人类识别正确率非常接近。
---
英文标题：
《Automated Epilepsy Diagnosis Using Interictal Scalp EEG》
---
作者：
Forrest Sheng Bao, Jue-Ming Gao, Jing Hu, Donald Y.-C. Lie, Yuanlin
  Zhang and K. J. Oommen
---
最新提交年份：
2009
---
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。
--
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。
--

---
英文摘要：
  Approximately over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much easier to collect than the ictal data, to automatically diagnose whether a person is epileptic. In our automated EEG recognition system, we extract three classes of features from the EEG data and build Probabilistic Neural Networks (PNNs) fed with these features. We optimize the feature extraction parameters and combine these PNNs through a voting mechanism. As a result, our system achieves an impressive 94.07% accuracy, which is very close to reported human recognition accuracy by experienced medical professionals.
---
PDF链接：
https://arxiv.org/pdf/0904.3808