## 3 Results and Discussion
### Case of Non-Parametric Analysis
The above-mentioned is a result that has caused motivation on the research of electrical signals in plants [19; 28; 29]. These stimuli generate significant electrical changes in the signal, which could explain the peak observed around sample 200. The results obtained are consistent with the biological nature of the signal, where a mixture of slow and fast processes was expected. These abrupt changes have been reported previously. It is on this feature that researchers have focused on determining key aspects of the electrical signal and is the motivation for this work [19]. After that, the time-frequency domain analysis of the _A. vera_ electrical signal was performed using the wavelet transform. Figure 4 shows the 3D graphical representations of the 16 combinations of wavelet functions and decomposition levels. Each sub-graph, organized in a \(4\times 4\) matrix, represents a specific configuration, where the X-axis corresponds to time, the Y-axis to the decomposition level, and the Z-axis to the magnitude of the wavelet coefficients. This design visually compares the different combinations, highlighting the properties of each wavelet function and its ability to represent the _A. vera_ electrical signal. The combination of the wavelet function \"db3\" with five levels, highlighted in red, showed an optimal balance between energy concentration and the representation of relevant signal details. In comparison, the other configurations, although similar in many respects, showed differences in the distribution of coefficients between levels. The previously mentioned is described in a similar way to that mentioned in [PERSON]'s work, where he used and tested only a single wavelet mother function to analyze the electrical signal from the plant. In this work, different functions and levels were tested to demonstrate and compare results [13]. In order to justify the best level decomposition, total energy and entropy were calculated. The numerical results, summarized in Table 2, support these observations. Figure 4: Decomposition of the electrical signal of _A. vera_ using different wavelet functions and levels. For the \"db3\" configuration with five levels, the total energy reached a value of 0.75573, showing that this combination captures a significant amount of the signal information. In addition, an entropy of 1.4221 shows an acceptable balance in the distribution of energy among the levels. In contrast, configurations with eight levels, such as \"db3\" (total energy of 0.82243 and entropy of 1.6614), capture more energy, but have a higher dispersion, which may lose key information from the electrical signal. On the other hand, the wavelet function \"sym4\" with five levels presented a similar behavior to \"db3\", with a slightly lower total energy (0.73798) and a comparable entropy (1.4092). This suggests that \"sym4\" could be a valid alternative for analysis, although its ability to capture smooth transitions in the signal appears to be inferior to \"db3\". In contrast, \"coif1\" showed lower total energy at low levels (0.46344 with four levels), and improved using five levels (0.76798), but its higher entropy (1.3407) indicates a lower concentration of energy at specific levels. Finally, \"haar\" was efficient for capturing transient events, but presented a lower total energy (0.72427 with five levels) and a lower entropy (1.2716), which emphasizes high-frequency events, at the cost of sacrificing low-frequency details. This makes it suitable for analysis focused on fast events, but less effective for characterizing global signal features. According to the above results, Figure 5 shows the decomposition into five levels using the mother function db3 of the Daubechies series. The black line is the original signal over time. The blue lines represent each of the five levels of decomposition. The first two levels (one and two) indicate low-frequency components. On the other hand, the later levels (from three to five) capture the abrupt changes already observed in the power spectrum obtained from the STFT.