# EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer's Disease
## 4 Discussion  
In this paper, we proposed a new method for the feature extraction in AD recognition from EEG signals. Our findings confirm that AD affects the power spectrum of the patient, according to previous studies [43; 44]. The proposed method is carried out in the time domain, exploiting the knowledge a priori of the EEG signals, i.e., power spectrum, spectral entropy, and phase synchronization. We used three different classification algorithms to validate our method, obtaining promising results. Indeed, the accuracy ranged between 73% and 97%, overcoming previous studies [24; 45]. In particular, in [45], the best accuracy rate was 94%, obtained by using discrete wavelet transform to extract features, whereas, in [24], we used a DT classifier (i.e., the C4.5 algorithm), reaching the following levels of accuracy: 83%, 92%, 79%, and 73% in HC vs. AD, HC vs. MCI, MCI vs. AD, and HC vs. MCI + AD classification problems, respectively. Furthermore, in the last two cases, we increased the level of accuracy, with values higher than 80% combining in the information gain filter. Here, K-NN was the best classification algorithm as concerning the accuracy (almost always greater than 80%), as shown in Figure 5, with a runtime of about 3 s (Table 2). On the contrary, the accuracy of DT was the lowest, and its running times the highest because of the computation complexity in the construction of the solution in a human interpretable format. The recent literature has reported several techniques using EEG signals for the early diagnosis of AD, differing in how features are extracted. Some of these, such as event-related potentials, signal a complexity analysis and relative power, involving a time domain signal analysis. Other techniques, instead, work in the Fourier domain, such as the coherence metric that evaluates the synchrony between two signals. In addition, there are techniques exploiting the analysis in the frequency domain, such as the continuous or discrete wavelet transform [7]. [PERSON] et al. [46] employed a linear neural unit with gradient descent adaptation as the filter to predict AD, achieving a specificity of 85% and a sensitivity between 85% and 94%, depending on the classifier. In Reference [47], the authors developed an algorithm that consists of three cascade methods for analysis: discrete WT, PSD, and coherence. They tested this method on 35 subjects by means of the bagged trees classifier trained with five-fold cross-validation, obtaining a 96.5% accuracy. There are only a limited number of works that have exploited time-frequency or bispectrum-based features, such as discriminating coefficients. The multimodal machine learning approach of [PERSON] et al. [48], where EEG signals are projected into the time-frequency domains by means of the continuous WT to extract a set of features from EEG sub-bands, while the nonlinear the phase-coupling information of EEG signals is also used to generate a second set of features from the bispectrum representation. This method provides high levels of accuracy with different classifiers in all considered problems: AD \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & \(f_{cut}\) **(Hz)** &
\begin{tabular}{c} **Time (s)** \\ **DT/SVM/K-NN** \\ \end{tabular} & **Tot. Time (s)** \\ \hline \multirow{2}{*}{AD vs. HC} & 7 & 21.8/14.0/3.0 & 38.8 \\  & 16 & 20.9/14.0/3.0 & 38.0 \\ \hline \multirow{2}{*}{AD vs. MCI} & 7 & 22.3/14.8/3.0 & 40.1 \\  & 16 & 21.1/15.0/3.0 & 39.1 \\ \hline \multirow{2}{*}{MCI vs. HC} & 7 & 20.7/13.3/3.0 & 37.0 \\  & 16 & 21.1/13.6/3.0 & 37.7 \\ \hline \multirow{2}{*}{AD + MCI vs. HC} & 7 & 20.6/16.4/3.0 & 40.0 \\  & 16 & 21.1/18.5/3.0 & 42.6 \\ \hline \multirow{2}{*}{AD vs. MCI vs. HC} & 7 & 25.0/22.6/4.0 & 51.6 \\  & 16 & 25.9/24.1/4.8 & 54.8 \\ \hline \hline \end{tabular} The Time column shows the execution time of each classifier, where DT = decision tree, SVM = support vector machine, and K-NN = K-nearest neighbors. The last column shows the sum of the execution times.  
\end{table}