Classification of biological signals based on nonlinear features

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.