摘要翻译：
虽然癫痫事件代表了与基线脑电图信号的主要偏差，但在记录的非癫痫部分可以看到癫痫形态特征。一个短暂的频率下降，称为慢化，通常是异常的，但不一定是癫痫性的脑电图变异。发作的终止通常与发作的峰值振幅和频率与恢复到基线之间的一段时间减慢有关。在TUH EEG癫痫发作语料库中的癫痫发作事件注释中，独立的减慢事件被确定为错误警报错误的主要来源。初步结果表明，难以自动区分癫痫发作事件和独立的减慢事件。TUH EEG减慢数据库是TUH EEG语料库的一个子集，在此被创建并介绍，以帮助开发一个癫痫检测工具，该工具可以区分癫痫发作结束时的减慢和独立的非癫痫发作减慢事件。该语料库包含100个10秒的样本，每一个背景，减慢，和癫痫事件。初步实验表明，在三种样本类型上训练的模型对癫痫检测的灵敏度为77%，而仅对癫痫和背景样本的灵敏度为43%。
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英文标题：
《Electroencephalographic Slowing: A Source of Error in Automatic Seizure
  Detection》
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作者：
Eva von Weltin, Tameem Ahsan, Vinit Shah, Dawer Jamshed, Meysam
  Golmohammadi, Iyad Obeid and Joseph Picone
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  Although a seizure event represents a major deviation from a baseline electroencephalographic signal, there are features of seizure morphology that can be seen in non-epileptic portions of the record. A transient decrease in frequency, referred to as slowing, is a generally abnormal but not necessarily epileptic EEG variant. Seizure termination is often associated with a period of slowing between the period of peak amplitude and frequency of the seizure and the return to baseline. In annotation of seizure events in the TUH EEG Seizure Corpus, independent slowing events were identified as a major source of false alarm error. Preliminary results demonstrated the difficulty in automatic differentiation between seizure events and independent slowing events. The TUH EEG Slowing database, a subset of the TUH EEG Corpus, was created, and is introduced here, to aid in the development of a seizure detection tool that can differentiate between slowing at the end of a seizure and an independent non-seizure slowing event. The corpus contains 100 10-second samples each of background, slowing, and seizure events. Preliminary experiments show that 77% sensitivity can be achieved in seizure detection using models trained on all three sample types compared to 43% sensitivity with only seizure and background samples.
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PDF链接：
https://arxiv.org/pdf/1801.0247