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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network
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A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network

机译：一种深时机器学习方法，利用时间增长神经网络对生物信号的循环时间序列进行分类

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摘要

This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk.
机译：本文提出了一种用于学习随机时间序列的循环内容的新方法：深度时间增长神经网络（DTGNN）。 DTGNN在不同的学习水平上结合了有监督和无监督的方法，以提高性能。多尺度学习结构使用它对循环时间序列（CTS）进行分类，其中以有效的方式保留了时间序列的动态内容。本文提出了一种系统的程序，该程序可用于查找一对多类应用程序的分类方法的设计参数。还提出了一种新颖的验证方法，可以定量和定性地评估结构风险。 DTGNN对分类器性能的影响通过使用来自不同医学应用的不同CTS组的重复随机子采样在统计学上得到验证。验证涉及四个医学数据库，包括108个脑电图信号记录，90个肌电图信号记录，130个心音信号记录和50个呼吸音信号记录。统计验证的结果表明，DTGNN显着提高了分类的性能，并且还表现出最佳的结构风险。

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