## 4 Conclusions  
Feature extraction is a key part in time series analyzing and classification procedures. Entropy measures are well-known time series features that are widely described in the literature. Traditional entropy measures are not appropriate for short and noisy time series. Moreover, they are sensitive to initial parameters that leads to inaccurate results. NNetEn overcomes these difficulties, and it has been applied to solving practical problems successfully. However, there were no functional modules for calculating NNetEn in common programming languages. To overcome this shortcoming, we apply Python language to implement the NNetEn algorithm. To increase the speed of calculations, the SARS-CoV-2-RBV1 dataset is used as the input data instead MNIST-10 dataset. Based on the NNetEn algorithm, three neural network entropy metrics (NNetEn, R2 Efficiency and Pearson Efficiency) are provided. The sine chaotic map, ANOVA and F-score are used for investigating the efficiency of new metrics in distinguishing between different signals. The results demonstrate that the new metrics are an efficient tool for distinguishing between the time series. The EEG classification of patients with Alzheimer's disease (36 people) and those in the control group (29 healthy people) is illustrated as a practical application. The SVM algorithm is used for the classification. For some channels, NNetEn ranks among the leaders in terms of feature strength; and on channel 11, it demonstrates the best results. Our computations confirm that a combination of classical entropy measures and NNetEn method provides results that are more accurate. Our computations demonstrate the synergistic effect of increasing classification accuracy when applying traditional entropy measures and the NNetEn concept conjointly. In practice, it is important to understand what entropies to use for signal separation and classification. However, each case requires its own parameters and types of entropies. NNetEn is the first entropy that does not use probability distribution function and provides useful information about the signal in the classification problems. For each individual task, a specific entropy measure must be tested, as it is difficult to predict how effective the measure will be in the given settings. Based on the examples presented, entropy depends in a complex way on the calculation parameters, and it is possible to extract useful information from a change in entropy. It is common for signals with similar \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Tools Used for Calculation**} & **Time (s)** & **Time (s)** \\  & **Dataset 1, \(\mu\) = 1** & **Dataset 2, \(\mu\) = 1** \\ \hline Delphi & 6.14 & 0.085 \\ \hline Python (NumPy + Numba) & 3.7 & 0.293 \\ \hline Python (NumPy) & 11.5 & 1.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average durations of the network training stage (Stage 9) using different programming languages. dynamics to have different entropy correlations based on their parameters. Implementing NNetEn in Python will allow the scientific community to apply the algorithm to identify a class of problems that are effectively solved by NNetEn. The review of previous studies demonstrates the NNetEn's success in practical problems. Moreover, NNetEn is illustrated with a practical example. In terms of feature strength, NNetEn ranks among the leaders for the classification of EEG signals from certain channels. The challenge of filling the reservoir matrix is the availability of many options. In Section 2.3, we have introduced six basic filling methods that affect the obtained results. Therefore, the method is considered as NNetEn input parameter. [1 shows the general concept of NNetEn calculation without specifying the method, in contrast to the algorithm defined in Figure 2. Therefore, the search for new types of reservoirs in the concept of NNetEn calculation can be a subject of further research.