## 2 Materials and Methods
### Feature Extraction  
To characterize the delimited signal, for each of the chosen windows, 34 signal features were calculated using Matlab(r) (version R2019b):  
Time domainAlong with the main descriptive statistical features, such as mean, minimum, maximum, variance, skewness, kurtosis and interquartile range, the following other statistical features were extracted: Hjorth Mobility [22] (characterizing the mean signal slope), Hjorth Complexity [22] (signal curvature), Generalized Hurst exponent [23] (long-term memory/dependency of the signal), the logarithmic and the Shannon wavelet packet entropy, all of them previously used for the analysis of the electrical plant signals and biological signals in general [11]. To broaden the analytical tools used in plant electrophysiology, features used in other signal analysis domains were also computed. These  
Figure 4: Schematic representation of the windowing procedure. At time point t (panel (**a**)) seven intervals of different lengths are taken from the signal, namely the last 15 s, 30 s, 1 min, 2 min, 5 min, 10 min and 30 min. Once the features are calculated for all of these seven windows, we move forward in a step of 5 min (panel (**b**)), where we take seven new intervals of the recorded signal with the same lengths as in the previous step. This procedure is repeated to cover the entire duration of the signal recording. include the root mean square of the window signal and the corresponding crest, shape, impulse and margin factor extracted as time-domain features [22; 24]. Frequency domainAiming at identifying and characterizing the eventual spectral changes in the signal due to induced plant stress, several statistical frequency-domain features were also calculated, namely frequency center, root variance frequency (considered as the first- and second-order moment of the Fourier spectrum respectively) and the root-mean-square frequency [22]. Color NoiseAs reported in recent literature [10; 25; 26], along with the eventual temporal dynamics, different external stimuli can change the type of color noise present in the plant electrical signal. To explore this further, we first generated the five color noises using the Matlab dsp_ColoredNoise function and calculated the corresponding fast Fourier transform (FFT). Then, we denoised the windowed signal applying the Matlab wden function (using Daubechies wavelet of order 4 and hard thresholding employing the principle of [PERSON]'s Unbiased Risk rule and rescaled on a single estimation of noise level based on first-level coefficients). The difference between the original windowed signal and the obtained denoised signal was considered as the signal's noise. Finally, we calculated the dot product between the normalized half-length FFT applied on the estimated noise and the normalized half-length color-noise FFT for each color noise, respectively. Time-frequency domainThe wavelet decomposition, representing a time-frequency domain characterization of a signal, is a recommended practice for analyzing non-stationary signals enabling more accurate tracking of both changes in signal amplitude and frequency. Consequently, for each window signal, we performed wavelet decompositions of order 8 using the Matlab modwtmra function for multiresolution analysis based on maximum overlap discrete wavelet transform (output of the Matlab modwt function), which allows a higher level of information in the wavelet decomposition by avoiding the process of subsampling [27]. We selected the decomposed signal at levels 1, 4 and 8, and we calculated their respective minimum, maximum and mean values. Table 1 lists the 34 calculated features for each of the seven windows. In total, 238 features per sample were extracted. The concatenation of all the 238 features from the 12 plants led to a total of 13,824 samples to analyze, with exactly half labelled as \"normal\" and half labelled as \"stressed\".