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首页> 外文会议>MELECON 2010;IEEE Mediterranean Electrotechnical Conference >Classification of biological signals based on nonlinear features
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Classification of biological signals based on nonlinear features

机译：基于非线性特征的生物信号分类

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摘要

The problem of patient disorder classification and prediction from biological signals is addressed. We approach the problem from the perspective of nonlinear dynamical systems. Explored signals are ECG and EEG. We propose a combination of linear and nonlinear features for classification of four different types of heart rhythms through heart rate variability analysis. Classification accuracy is evaluated by three well-known machine learning algorithms: C4.5, support vector machines and random forest. The algorithms' success rates are compared. The method of combining linear and nonlinear measures shows promising results in heart rate variability modeling. Random forest method has exhibited 99.6% classification accuracy.
机译：解决了患者疾病分类和根据生物学信号进行预测的问题。我们从非线性动力学系统的角度来解决这个问题。探索的信号是ECG和EEG。我们建议通过线性和非线性特征的组合，通过心率变异性分析对四种不同类型的心律进行分类。分类准确性由三种著名的机器学习算法评估：C4.5，支持向量机和随机森林。比较算法的成功率。线性和非线性测量相结合的方法在心率变异性建模中显示出可喜的结果。随机森林法显示出99.6％的分类准确率。

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