###### Abstract
Here, we used 60,000 handwritten images for training and 10,000 handwritten images for testing the proposed entropy accuracy. Several studies have tested this method successfully [29; 31; 32; 33; 34; 35]. [PERSON] et al. considered NNetEn as the characteristic of ship-radiated noise signals [31]. [PERSON] used NNetEn of electroencephalogram (EEG) signals for classifying motor imaginary of end users with disability [32]. He found that eight channels are enough for the classification, while other existed methods considered 30 channels. [PERSON] et al. introduced two-dimensional NNetEn for remote sensing imaginary and geophysical mapping [33]. NNetEn successfully performed analyzing the dynamic of chaotic spike oscillator based on S-switch [34] and measuring the complexity of response of traveling ionospheric disturbances under geomagnetic storms [35]. One of the disadvantages of the method is the high computation time associated with training and testing the network using the MNIST-10 database (stages 9, 10). To reduce computational overhead, we tested the method to reduce MNIST database size and used SARS-CoV-2-RBV1 dataset [36] as the input dataset in Stage 2. Figure 1: NNetEn calculation concept. The concept demonstrates the sequence of steps for computing NNetEn, including writing a time series to the reservoir, transforming the database in the reservoir through the time series, training and testing the output classifier, converting classification metric into NNetEn entropy. Compared to the other entropy measures, the NNetEn has a unique feature to compute the dynamic behavior of the input data of either short or longer length and gives a meaningful information about the input data. In previous studies, NNetEn was computed using Delphi software, however, it is impractical to expect the researchers to be familiarized with this software tool. Hence, we aim to calculate the NNetEn measure using a widely used programming language Python. In this study, we have developed Python programming code to compute the NNetEn, tested the tool with different types of input data, and benchmarked the performance of the proposed method with the state-of-the-art methods in the literature. The NNetEn's ability to differentiate signals was investigated on synthetic signals based on sinus mapping. In addition, we analyzed the EEG recordings obtained at AHEPA General Hospital of Thessaloniki's Department of Neurology [37]. Information on the functioning of the human brain can help to develop such tools as human-machine interface (HMI) and clinical diagnosis systems (CDS) [38]. Besides analyzing cognitive processes (emotional state, mental fatigue level, emotional stress, mental health), it is possible to detect the presence of a number of neurological diseases (epilepsy, Alzheimer's disease, attention deficit/hyperactivity disorder, bipolar disorder, etc.) [38]. In order to classify EEG signals, it is necessary to extract features from the original EEG signal. Methods to extract features include spectral analysis [39], wavelet analysis [40], entropy [41], statistical analysis [42], and others. Following the feature extraction, signals are classified using machine learning algorithms, such as a nearest neighbor method, a support vector machine, a decision tree, a random decision forest, neural networks, etc. [38; 43]. In this paper, the robustness of NNetEn feature in analyzing EEG signal is investigated by considering AHEPA dataset. The AHEPA dataset includes electroencephalograms from 88 patients divided into three groups: control group (29 people), dementia (23), and Alzheimer's disease patients (36 people). Later, the NNetEn extracted from the above dataset has been processed and classified through a conventional machine learning algorithm (Support Vector Machine (SVM)). The performance of the NNetEn feature was analyzed with other conventional entropy measures.