# EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer's Disease
## 4 Discussion

Table 2: Average execution time of the classification algorithm, including tuning of the hyperparameters. vs. HC, Ad vs. MCI, MCI vs. HC, and AD vs. MCI vs. HC and on a large cohort of subjects (i.e., 189 subjects: 63 AD, 63 MCI, and 63 HC). Similar to our method for the early detection of AD, the Lacosogram tool [21] performs a statistical analysis to measure distances between EEG sub-bands, obtaining an accuracy of 98.06% for HC vs. MCI, 95.99% for HC vs. AD, and 93.85% for MCI vs. AD. [PERSON] and [PERSON] in [49] decomposed EEG by using the WT to decompose the EEG signal into its five sub-bands. The means and variances of the wavelet coefficients were evaluated and used as input to a SVM classifier, achieving an accuracy value of 88% in AD vs. HC classification. Although, in our previous work [24], the experimental results showed that wavelet coefficients evaluated by applying the discrete wavelet transform achieved the highest accuracy rates (i.e., 83.3% for AD vs. HC, 91.7% for MCI vs. HC, and 79.1% for AD vs. MCI), the three-class classification did not achieve good results when we used only Fourier or Wavelet transform. On the contrary, here, the results of the three-class classification problem (AD vs. MCI vs. HC) were also reported, showing an average accuracy of 78% for the three classifiers and longer running times. This further proved the validity of this feature extraction method, which plays a key role in the analysis. Indeed, the method separates the high frequencies and the low frequencies of the EEG signal, and then, it computes their powers. The comparison of these powers shows an imbalance of energies in the frequency range, demonstrating the phenomenon of the STTL described in [15]. Our findings showed that a correct choice of \(f_{cut}\) increases the accuracy of discrimination between AD, MCI, and HC subjects. In addition, the proposed method for feature extraction is simple and fast in running time, and therefore, it is easily replicable in different development environments. This is undoubtedly its greatest strength, making its implementation and understanding very easy. The model, tested on a larger sample, could lead to the identification of biomarkers capable of determining features that discriminate electrical signals between different AD cohorts at specific electrodes. As a future work, we plan to improve the classification method to the point of removing all the HC subjects, applying double filtering with \(f_{cut}=7\) Hz from the main sample, and distinguish AD from MCI by applying double filtering with \(f_{cut}=16\) Hz. However, given the heterogeneity of the disease, a larger cohort is necessary to confirm the results of this study. We also plan to test the method in different EEG recording protocols, maybe while the subject performs a cognitive task, in order to provide insights on how AD affects certain cognitive areas. Since AD is expected to affect a large part of the worldwide population in the following years, EEG represents a suitable technique to assist clinicians in an early diagnosis. From this perspective, the method could be implemented on embedded devices and used in real time during EEG signal acquisition due to its low computational resource requirements. Considering the simplicity and robustness of the double filtering, we could promote it as an inexpensive and portable software suite by programming the current embedded electronic microprocessor, such as a Dev Board [50]. Conceptualization, E.W. and D.P. ; methodology, D.P. ; software, D.P. and P.D.P. ; validation, E.W., M.C.D.C. and S.D.S. ; formal analysis, E.W. and D.P. ; investigation, M.C.D.C. ; resources, M.C.D.C. ; data curation, S.D.S. ; writing--original draft preparation, D.P., M.C.D.C. and E.W. ; writing--review and editing, E.W. and M.C.D.C. ; supervision, E.W. and M.C.D.C. ; project administration, E.W. and M.C.D.C. ; and funding acquisition, M.C.D.C. All authors have read and agreed to the published version of the manuscript.