Open Access Open Access  Restricted Access Subscription or Fee Access

Alzheimer disorders diagnosis system design using machine learning for EEG signal

Usha B. Patel, Vandana V. Patel


The diagnosis of Alzheimer's disorders (AD), a prevalent neurological disorder, can created by utilising a range of therapeutic methods, including the electroencephalogram (EEG), which has been especially successful in the past. The objective for this study is to develop a computer-aided diagnosis tool which may recognize AD from EEG data. The EEG information was cleaned up with a band-pass elliptic digital filter to remove any interference or disruptions. The filtered signal was then divided into its frequency ranges using the Discrete Wavelet Transform (DWT) method, enabling the extraction of EEG signal characteristics. To enhance the diagnostic performance, various signal features were integrated into the DWT technique, including logarithmic band power (LBP), standard deviation, and root mean square (RMS). These features were incorporated to generate feature vectors that were utilized in the diagnosis process. In order to categorize the EEG features into their respective classes, various machine learning (ML) approaches were explored, including quadratic discriminant analysis (QDA), support vector machine (SVM), and k-nearest neighbour (KNN). The performance of these approaches was then evaluated by comparing and computing their sensitivity, specificity, and overall diagnosis accuracy. When comparing the accuracy values, it can be observed that QDA has a higher accuracy of 97.5% compared to KNN's accuracy of 95%.

Full Text:



Hill JM, Lukiw WJ. Microbial-generated amyloids and Alzheimer’s disease (AD). Front Aging Neurosci [Internet]. 2015 Feb 10 [cited 2023 Apr 3];7. Available from:

Guerreiro R, Bras J. The age factor in Alzheimer’s disease. Genome Med. 2015 Dec;7(1):106.

Swerdlow RH, Burns JM, Khan SM. The Alzheimer’s disease mitochondrial cascade hypothesis: Progress and perspectives. Biochim Biophys Acta BBA - Mol Basis Dis. 2014 Aug;1842(8):1219–31.

Tait L, Tamagnini F, Stothart G, Barvas E, Monaldini C, Frusciante R, et al. EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease. Sci Rep. 2020 Oct 19;10(1):17627.

Cassani R, Falk TH, Fraga FJ, Cecchi M, Moore DK, Anghinah R. Towards automated electroencephalography-based Alzheimer’s disease diagnosis using portable low-density devices. Biomed Signal Process Control. 2017 Mar;33:261–71.

Koenig T, Smailovic U, Jelic V. Past, present and future EEG in the clinical workup of dementias. Psychiatry Res Neuroimaging. 2020 Dec;306:111182.

M. Ghazal T, Abbas S, Munir S, A. Khan M, Ahmad M, F. Issa G, et al. Alzheimer Disease Detection Empowered with Transfer Learning. Comput Mater Contin. 2022;70(3):5005–19.

Huggins CJ, Escudero J, Parra MA, Scally B, Anghinah R, Vitória Lacerda De Araújo A, et al. Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing. J Neural Eng. 2021 Aug 1;18(4):046087.

Pirrone D, Weitschek E, Di Paolo P, De Salvo S, De Cola MC. EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer’s Disease. Appl Sci. 2022 May 26;12(11):5413.

Feng C, Elazab A, Yang P, Wang T, Zhou F, Hu H, et al. Deep Learning Framework for Alzheimer’s Disease Diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access. 2019;7:63605–18.

Ramzan F, Khan MUG, Rehmat A, Iqbal S, Saba T, Rehman A, et al. A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks. J Med Syst. 2020 Feb;44(2):37.

Puente-Castro A, Fernandez-Blanco E, Pazos A, Munteanu CR. Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques. Comput Biol Med. 2020 May;120:103764.

Amezquita-Sanchez JP, Mammone N, Morabito FC, Marino S, Adeli H. A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals. J Neurosci Methods. 2019 Jul;322:88–95.

AlSharabi K, Bin Salamah Y, Abdurraqeeb AM, Aljalal M, Alturki FA. EEG Signal Processing for Alzheimer’s Disorders Using Discrete Wavelet Transform and Machine Learning Approaches. IEEE Access. 2022;10:89781–97.

Xu Q, Zhou H, Wang Y, Huang J. Fuzzy support vector machine for classification of EEG signals using wavelet-based features. Med Eng Phys. 2009 Sep;31(7):858–65.

Bablani A, Edla DR, Dodia S. Classification of EEG Data using k-Nearest Neighbor approach for Concealed Information Test. Procedia Comput Sci. 2018;143:242–9.

Al Ghayab HR, Li Y, Siuly S, Abdulla S. Epileptic EEG signal classification using optimum allocation based power spectral density estimation. IET Signal Process. 2018 Aug;12(6):738–47.



  • There are currently no refbacks.

Copyright (c) 2023 Journal of Control & Instrumentation

eISSN: 2229-6972