## 3 Numerical Results and Discussion
### EEG Signal Separation  
#### 3.4.1 Selection of the Most Informative Component of the EEG Signal  
As described in Section 2.1, this work utilizes the Dataset 3 to study EEG signal separation. Due to the complexity of the problem, it was decided to use only the records of patients with Alzheimer's disease (36 people) and those in the control group (29 healthy people). Because the number of patients in these records is small, six non-overlapping segments were selected from each record, each of which represented an independent observation. All segments lasted 10 s and had the same duration. As the original dataset contained time stamps indicating unwanted patient activity (muscle movements, swallowing, blinking), the segments were selected to avoid overlap with the indicated activity periods.  
The EEG signal's entropy may be a sign of poor separation ability since it has a wide spectral range (0-250 Hz), and the main information about brain activity lies in a relatively narrow set of frequencies [50]. The wavelet transform can be used to decompose the EEG signal into separate frequency components and increase the information content. A 5 th-order digital Butterworth filter was used to pre-filter the signal (lower frequency 0.5 Hz, upper frequency 32 Hz) [51]. In this case, the discrete wavelet transform (DWT) of the signal was carried out using the \(db4\) wavelet and performed six level decomposition.  
A total of 14 options were considered to select the most informative component of the signal:  
Figure 13: Dependences of entropy values NNetEn\({}_{\text{uv}}\)(r1), their difference (NNetEn\({}_{\text{uv}}\)(r2)—NNetEn\({}_{\text{uv}}\)(r1)) and standard deviation S(r1) for Dataset 1 (**a**) and Dataset 2 (**b**). Figure reflects the reason for the sharp change in F-ratio (in Figure 12).  
1: The original unfiltered \(X_{\text{EEG}}\)(t) signal.  
2: \(X\)F\({}_{\text{EEG}}\)(t) filtered signal.  
3-8: Approximation coefficients (A1-A6) of the wavelet transform of the filtered signal.  
9-14: Detail coefficients (D1-D6) of the wavelet transform of the filtered signal.  
Then, 14 datasets were created, each containing 390 records (65 patients, 6 segments) and 19 features--the SVDEn (\(m\) = 2, \(delay\) = 1) value for 19 channels, followed by a class label of 0 indicating a control group patient and 1 indicating a patient with Alzheimer's disease patient. In order to determine the informativeness of the signals, an analysis of variance (ANOVA) was conducted with a significance level (\(p\)) of 0.05. A maximum F-ratio (\(F_{m}\)) value was used to select the most informative signal type.  
Figure 14 shows the \(F_{m}\) values for all types of signals. Among all types of filtered signals, the approximation coefficient A3 (0-32.5 Hz) provides the most information. Additionally, the coefficients A1, A2 and A4, as well as XFEEG(t) have relatively high \(F_{m}\) values. In terms of detail factor analysis, D4 (16.25-32.5 Hz) is the most informative. The coefficients A6, D1 and D2 do not allow us to state that the EEG signals from healthy and sick patients differ using SVDEn, as they are greater than the significance level (\(p\) = 0.05).