Entropy measures are effective features for time series classification
problems. Traditional entropy measures, such as Shannon entropy, use
probability distribution function. However, for the effective separation of
time series, new entropy estimation methods are required to characterize the
chaotic dynamic of the system. Our concept of Neural Network Entropy (NNetEn)
is based on the classification of special datasets (MNIST-10 and
SARS-CoV-2-RBV1) in relation to the entropy of the time series recorded in the
reservoir of the LogNNet neural network. NNetEn estimates the chaotic dynamics
of time series in an original way. Based on the NNetEn algorithm, we propose
two new classification metrics: R2 Efficiency and Pearson Efficiency. The
efficiency of NNetEn is verified on separation of two chaotic time series of
sine mapping using dispersion analysis (ANOVA). For two close dynamic time
series (r = 1.1918 and r = 1.2243), the F-ratio has reached the value of 124
and reflects high efficiency of the introduced method in classification
problems. The EEG signal classification for healthy persons and patients with
Alzheimer disease illustrates the practical application of the NNetEn features.
Our computations demonstrate the synergistic effect of increasing
classification accuracy when applying traditional entropy measures and the
NNetEn concept conjointly. An implementation of the algorithms in Python is
presented.

本文提出了基于 NNetEn 算法的两种新分类指标：R2 Efficiency 和 Pearson Efficiency，以及将传统熵测量和 NNetEn 特征应用于时间序列分类中的成功实现，通过实验验证了该方法在分类问题中的高效性。