摘要翻译：
脑电图(EEG)作为癫痫发作分类最常用的工具，包含了大脑不同生理状态的有用信息。脑电信号中的癫痫相关特征在时频基预测中可以更好地识别。本文提出了一种新的基于小波包(WPs)的癫痫发作分类方法，该方法通过对母小波函数和小波包基进行后验调整来改进癫痫发作分类。采用支持向量机(SVM)作为分类器，对癫痫发作和非癫痫发作的脑电片段进行分类。为了评价提出的算法，使用了一个公开的数据集，该数据集包含了不同的癫痫患者和健康个体。实验结果表明，本文提出的方法在癫痫发作分类方面优于以前提出的一些算法。
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英文标题：
《A New Method for Epileptic Seizure Classification in EEG Using Adapted
  Wavelet Packets》
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作者：
Amirmasoud Ahmadi, Vahid Shalchyan, Mohammad Reza Daliri
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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：
  Electroencephalography (EEG), as the most common tool for epileptic seizure classification, contains useful information about different physiological states of the brain. Seizure related features in EEG signals can be better identified when localized in time frequency basis projections. In this work, a novel method for epileptic seizure classification based on wavelet packets (WPs) is presented in which both mother wavelet function and WP bases are adapted a posteriori to improve the seizure classification. A support vector machine (SVM) as classifier is used for seizure versus non-seizure EEG segment classification. In order to evaluate the proposed algorithm, a publicly available dataset containing different groups patient with epilepsy and healthy individuals are used. The obtained results indicate that the proposed method outperforms some previously proposed algorithms in epileptic seizure classification.
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PDF链接：
https://arxiv.org/pdf/1805.04703