Abstract
Alzheimer’s disease causes most of dementia cases. Although currently there is no cure for this disease, predicting the cognitive decline of people at the first stage of the disease allows clinicians to alleviate its burden. Clinicians evaluate individuals’ cognitive decline by using neuropsychological tests consisting of different sections, each devoted to testing a specific set of cognitive skills. In this paper, we present the results of a preliminary study aimed at assessing the ability of machine learning based tools to predict the cognitive decline of Alzheimer’s patients using features extracted from EEG records at resting state. We tested seven classification schemes in predicting nine scores, provided by different sections of four neuropsychological tests. The experimental results demonstrated that at least three of these scores allows EEG-based features to be effective in predicting the cognitive decline of Alzheimer’s patients by using machine learning tools.
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References
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, Heidelberg (2006)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Breiman, L., Friedman, J., Stone, C.J., Olshen, R.A.: Classification and Regression Trees. CRC Press, Boca Raton (1984)
le Cessie, S., van Houwelingen, J.C.: Ridge estimators in logistic regression. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 41(1), 191–201 (1992)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1-27:27 (2011)
Cilia, N., De Stefano, C., Fontanella, F., Marrocco, C., Molinara, M., Scotto Di Freca, A.: An end-to-end deep learning system for medieval writer identification. Pattern Recogn. Lett. 129, 137–143 (2020). https://doi.org/10.1016/j.patrec.2019.11.025
Cilia, N.D., De Gregorio, G., De Stefano, C., Fontanella, F., Marcelli, A., Parziale, A.: Diagnosing Alzheimer’s disease from on-line handwriting: a novel dataset and performance benchmarking. Eng. Appl. Artif. Intell. 111, 104822 (2022). https://doi.org/10.1016/j.engappai.2022.104822
De Stefano, C., Ferrigno, L., Fontanella, F., Gerevini, L., Molinara, M.: Evolutionary computation to implement an IoT-based system for water pollution detection. SN Comput. Sci. 3(2), 1–15 (2022)
Fiscon, G., et al.: Combining EEG signal processing with supervised methods for Alzheimer’s patients classification. BMC Med. Inform. Decis. Mak. 18(1), 1–10 (2018)
Gauthier, S., Rosa-Neto, P., Morais, J., Webster, C.: World Alzheimer report 2021 journey through the diagnosis of dementia (2021). https://www.alzint.org/u/World-Alzheimer-Report-2021.pdf
Grueso, S., Viejo-Sobera, R.: Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: a systematic review. Alzheimers Res. Ther. 13(1) (2021)
Hampel, H., et al.: Perspective on future role of biological markers in clinical therapy trials of Alzheimer’s disease: a long-range point of view beyond 2020. Biochem. Pharmacol. 88(4), 426–449 (2014)
James, C., Ranson, J., Everson, R., Llewellyn, D.: Performance of machine learning algorithms for predicting progression to dementia in memory clinic patients. JAMA Netw Open 4(12) (2021)
Jeong, J.: EEG dynamics in patients with Alzheimer’s disease. Clin. Neurophysiol. 115(7), 1490–1505 (2004)
Rey, A.: L’examen clinique en psychologie. Presses universitaires de France (1964)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)
Sanei, S., Chambers, J.: EEG Signal Processing. Wiley, Hoboken (2007)
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Fontanella, F. et al. (2022). Machine Learning to Predict Cognitive Decline of Patients with Alzheimer’s Disease Using EEG Markers: A Preliminary Study. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_12
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