EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer’s Disease

• Published in: Applied sciences Vol. 12; no. 11; p. 5413
• Main Authors: Pirrone, Daniele, Weitschek, Emanuel, Di Paolo, Primiano, De Salvo, Simona, De Cola, Maria Cristina
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2022
• Subjects: Alzheimer's disease; Biomarkers; Classification; Cognition & reasoning; Dementia; EEG signals; Electrodes; Electroencephalography; FIR filtering; Machine learning; power spectrum; Signal processing; supervised machine learning; Wavelet transforms
• DOI: 10.3390/app12115413
• ISSN: 2076-3417

Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). In the last years, EEG signal analysis has become an important topic of research to extract suitable biomarkers to determine the subject’s cognitive impairment. In this work, we propose a novel simple and efficient method able to extract features with a finite response filter (FIR) in the double time domain in order to discriminate among patients affected by AD, MCI, and healthy controls (HC). Notably, we compute the power intensity for each high- and low-frequency band, using their absolute differences to distinguish among the three classes of subjects by means of different supervised machine learning methods. We use EEG recordings from a cohort of 105 subjects (48 AD, 37 MCI, and 20 HC) referred for dementia to the IRCCS Centro Neurolesi “Bonino-Pulejo” of Messina, Italy. The findings show that this method reaches 97%, 95%, and 83% accuracy when considering binary classifications (HC vs. AD, HC vs. MCI, and MCI vs. AD) and an accuracy of 75% when dealing with the three classes (HC vs. AD vs. MCI). These results improve upon those obtained in previous studies and demonstrate the validity of our approach. Finally, the efficiency of the proposed method might allow its future development on embedded devices for low-cost real-time diagnosis.