摘要翻译：
通过分析脑电图(EEG)可以诊断癫痫等脑相关疾病。然而，手工分析脑电图数据需要训练有素的临床医生，并且是一个已知的相对较低的评分者之间协议(IRA)的程序。此外，数据量和新数据的出现速度使人工解释成为一个耗时、耗资和昂贵的过程。相反，脑电数据的自动分析通过缩短诊断时间和减少人工错误提供了改善病人护理质量的潜力。在这篇论文中，我们将重点放在解释一个EEG会话的第一步之一--识别大脑活动是异常还是正常。为了解决这一问题，我们提出了一种新的递归神经网络(RNN)结构ChronoNet，它受到图像分类领域最新发展的启发，旨在有效地处理脑电数据。Chronet是通过堆叠多个1D卷积层和随后的深选通循环单元(GRU)层而形成的，其中每个1D卷积层使用多个长度呈指数变化的滤波器，堆叠的GRU层以前馈方式密集连接。我们使用最近发布的TUH异常EEG语料库数据集来评估Chroneet的性能。与以前使用该数据集的研究不同，CronoNet直接将时间序列EEG作为输入，并学习大脑活动模式的有意义的表示。ChroneT比以前报告的最佳结果高出7.79%，从而为该数据集设置了一个新的基准。此外，我们成功地将Chronet应用于语音命令分类，从而证明了Chronet的领域无关性。
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英文标题：
《ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG
  Identification》
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作者：
Subhrajit Roy, Isabell Kiral-Kornek, and Stefan Harrer
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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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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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英文摘要：
  Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEG). However, manual analysis of EEG data requires highly trained clinicians, and is a procedure that is known to have relatively low inter-rater agreement (IRA). Moreover, the volume of the data and the rate at which new data becomes available make manual interpretation a time-consuming, resource-hungry, and expensive process. In contrast, automated analysis of EEG data offers the potential to improve the quality of patient care by shortening the time to diagnosis and reducing manual error. In this paper, we focus on one of the first steps in interpreting an EEG session - identifying whether the brain activity is abnormal or normal. To solve this task, we propose a novel recurrent neural network (RNN) architecture termed ChronoNet which is inspired by recent developments from the field of image classification and designed to work efficiently with EEG data. ChronoNet is formed by stacking multiple 1D convolution layers followed by deep gated recurrent unit (GRU) layers where each 1D convolution layer uses multiple filters of exponentially varying lengths and the stacked GRU layers are densely connected in a feed-forward manner. We used the recently released TUH Abnormal EEG Corpus dataset for evaluating the performance of ChronoNet. Unlike previous studies using this dataset, ChronoNet directly takes time-series EEG as input and learns meaningful representations of brain activity patterns. ChronoNet outperforms the previously reported best results by 7.79% thereby setting a new benchmark for this dataset. Furthermore, we demonstrate the domain-independent nature of ChronoNet by successfully applying it to classify speech commands.
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PDF链接：
https://arxiv.org/pdf/1802.00308