癫痫发作检测：一种深度学习方法

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摘要翻译：
癫痫是仅次于偏头痛的第二大常见脑部疾病。癫痫发作的自动检测可以大大提高患者的生活质量。目前基于脑电图(EEG)的癫痫检测系统在现实生活中遇到了许多挑战。EEGs是非平稳信号，癫痫发作模式因患者和记录期而异。此外，脑电数据容易出现多种噪声类型，这些噪声类型对癫痫发作的检测精度有负面影响。为了应对这些挑战，我们介绍了一种基于深度学习的方法，该方法自动学习癫痫发作的鉴别脑电特征。具体来说，为了揭示连续数据样本之间的相关性，首先将时间序列EEG数据分割成一系列不重叠的EEG。其次，利用长短时记忆网络学习正常脑电和癫痫发作脑电模式的高级表征。第三，将这些表示输入到Softmax函数中进行训练和分类。在一个著名的基准临床数据集上的结果表明，所提出的方法优于现有的最先进的方法。此外，我们的方法被证明是鲁棒的噪声和现实生活的条件下。与现有的对噪声相当敏感的检测方法相比，该方法在常见脑电伪影（肌肉活动和眨眼）以及白噪声的情况下仍然保持了较高的检测性能。
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英文标题：
《Epileptic Seizure Detection: A Deep Learning Approach》
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作者：
Ramy Hussein, Hamid Palangi, Rabab Ward, Z. Jane Wang
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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：
  Epilepsy is the second most common brain disorder after migraine. Automatic detection of epileptic seizures can considerably improve the patients' quality of life. Current Electroencephalogram (EEG)-based seizure detection systems encounter many challenges in real-life situations. The EEGs are non-stationary signals and seizure patterns vary across patients and recording sessions. Moreover, EEG data are prone to numerous noise types that negatively affect the detection accuracy of epileptic seizures. To address these challenges, we introduce the use of a deep learning-based approach that automatically learns the discriminative EEG features of epileptic seizures. Specifically, to reveal the correlation between successive data samples, the time-series EEG data are first segmented into a sequence of non-overlapping epochs. Second, Long Short-Term Memory (LSTM) network is used to learn the high-level representations of the normal and the seizure EEG patterns. Third, these representations are fed into Softmax function for training and classification. The results on a well-known benchmark clinical dataset demonstrate the superiority of the proposed approach over the existing state-of-the-art methods. Furthermore, our approach is shown to be robust in noisy and real-life conditions. Compared to current methods that are quite sensitive to noise, the proposed method maintains its high detection performance in the presence of common EEG artifacts (muscle activities and eye-blinking) as well as white noise.
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PDF链接：
https://arxiv.org/pdf/1803.09848

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