Classification of Stationary Signals with Mixed Spectrum

IF 1.2 4区数学

International Journal of Biostatistics Pub Date : 2011-01-06 DOI:10.2202/1557-4679.1288

P. Saavedra, A. Santana-del-Pino, C. N. Hernández-Flores, J. Artiles-Romero, J. J. González-Henríquez

引用次数: 0

Abstract

This paper deals with the problem of discrimination between two sets of complex signals generated by stationary processes with both random effects and mixed spectral distributions. The presence of outlier signals and their influence on the classification process is also considered. As an initial input, a feature vector obtained from estimations of the spectral distribution is proposed and used with two different learning machines, namely a single artificial neural network and the LogitBoost classifier. Performance of both methods is evaluated on five simulation studies as well as on a set of actual data of electroencephalogram (EEG) records obtained from both normal subjects and others having experienced epileptic seizures. Of the different classification methods, Logitboost is shown to be more robust to the presence of outlier signals.

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混合频谱平稳信号的分类

本文研究了具有随机效应和混合谱分布的平稳过程产生的两组复信号的判别问题。还考虑了异常信号的存在及其对分类过程的影响。作为初始输入，提出了由谱分布估计得到的特征向量，并将其用于两种不同的学习机，即单个人工神经网络和LogitBoost分类器。这两种方法的性能通过五个模拟研究以及从正常受试者和其他经历过癫痫发作的人获得的脑电图(EEG)记录的一组实际数据进行了评估。在不同的分类方法中，Logitboost对异常信号的存在表现出更强的鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Biostatistics Mathematics-Statistics and Probability

CiteScore

2.30

自引率

8.30%

发文量

期刊介绍： The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.