# EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer's Disease
## 3 Results  
In this section, we report the results obtained after any steps of our method, e.g., from the feature extraction to the classification process, providing the accuracy measures of the classification algorithms. First, we show in Figure 3 the allocation of the power spectrum for each patient. Looking closely at Figure 3, we can enhance that the PSD in AD and MCI is restricted in low frequencies (<7 Hz), while, in HC, the power is spread up to about 14 Hz. Thus, we expect that the next part of the feature extraction procedure also identifies that these frequencies are the best cutoffs for a good separation between classes. As viewable in Figure 4a, the statistical test carried out on the characteristics shows a good separation between classes in these \(f_{cut}\) values. Indeed, we found an excellent class separation between HC and AD-MCI for the value of \(f_{cut}\) equal to 7 Hz, as can be seen in Figure 4b, and an excellent separation between AD and MCI with \(f_{cut}\) = 16 Hz (Figure 4c). Figure 3: Computed pspectrum from 19 biopotentials (EEG signals) in a frequency range of 0–20 Hz for AD (**a**), MCI (**b**), and HC (**c**). The dashed red line separates each individual signal from the 19 biopotentials that make up the preprocessed EEG signal. These interesting results suggest applying the first filtering at 7 Hz to exclude controls and the subsequent filtering at 16 Hz for a better classification between MDI and AD. The classification of the samples achieves a high level of accuracy, especially in distinguishing AD vs. HC and MDI vs. HC cases. In fact, the three classification procedures achieve an accuracy value of more than 87% and up to 97%, as shown in Figure 5a,b. Moreover, when we consider AD + MCI vs. HC, the accuracy reaches a value between 84% and 89% (Figure 5c). The effectiveness of the proposed method consists of tunneling \(f_{cut}\), as shown in Figure 5d for the AD vs. MCI case. Here, the accuracy of the classification methods improves from 49-60% to 80-83% when \(f_{cut}\) is increased from 7 Hz to 16 Hz. Finally, Figure 5e shows the comparison between the three different cases and reinforces the hypothesis of the effectiveness of the proposed method for feature extraction. In the latter case, the accuracy value is between 73% and 86%. Figure 4: (**a**) The mean value of the feature extraction, the black double arrows, show the maximum distance between HC and AD + MCI for \(f_{cut}=7\) Hz and between AD and MCI for \(f_{cut}=16\) Hz. The statistical results are shown in (**b**) for 7 Hz and (**c**) for 16 Hz. In both of the last two graphs, the main box represents the data distribution, while the red line represents the median value, and the whisker stretches from the box show the range of the data, except for the outlays that are represented, such as the floating point (+). In Table 2, we present the computation times of the classification procedure. The costs of feature extraction are low, and the computation time is about 0.1 s for a single subject. On the contrary, in the classification process, the computation time is higher because of the search for the best hyperparameters improving the accuracy. In the binary classification problems, the total execution time is, on average, 39.5 s, of which the DT takes about 21 s, the SVM takes about 15 s, and the K-NN takes about 3 s, except in the last case, when the execution time increases by two seconds. K-NN is the algorithm with the lowest runtime, while DT is the slowest. This is because KNN stores the training data in an n-dimensional space defining the pattern's spaces, and for each unknown sample, it assigns the pattern's space with a minor distance function [42] while DT extracts a classification model composed of features and value assignments requiring a longer runtime, although DT provides a better interpretation of the classification solution.