Identification of Alzheimer’s Disease Using Novel Dual Decomposition Technique and Machine Learning Algorithms from EEG Signals

Digambar Puri, Sanjay Nalbalwar, Anil Nandgaonkar, Jaswantsing Rajput, Abhay Wagh


Alzheimer’s disease (AD) is one of the neurodegenerative disorders. The rate of AD prevalence is rapidly increasing worldwide. The existing clinical invasive methods and neuro-imaging techniques to detect AD are time-consuming, subjective, and expensive. To overcome these issues, we proposed a new automatic framework for detecting AD at an early stage based on the dual decomposition method. Initially, EEG signals of mild cognitive impairment (MCI), AD, and normal control (NC) patients are divided into five subbands by employing discrete wavelet transform (DWT). Subsequently, a Variational mode decomposition (VMD) is applied to these five EEG subbands for further decomposition into various intrinsic mode functions (IMFs). Afterward, three different multiscale permutation entropy (PE) features, namely Shannon PE (SPE), Tsalli’s PE (TPE), and Renyi PE (RPE), have been measured from each IMF. Later, these features have been used to train and test ensemble bagged tree (EBT), k-nearest neighbor, support vector machine (SVM), decision tree (DT), and neural networks with a 10-fold cross-validation scheme. The proposed method has been verified using EEG signals of 59-AD, 7-MCI, and 102-NC subjects. The results obtained from the proposed DWT-VMD method provide 95.20% accuracy for three-class and 97.70% for two-class classification using an EBT classifier with 10-fold cross-validation. It shows a significant ability to distinguish AD from MCI. The proposed dual decomposition method can employ for other neurodegenerative disorders such as Parkinson’s disease, epilepsy, various sleep disorders, and major depressive disorders.


Alzheimer’s disease (AD); support vector machine (SVM); electroencephalogram (EEG); ensemble bagged tree (EBT)

Full Text:



A. Atri, “The Alzheimer’s Disease Clinical Spectrum: Diagnosis and Management,†Medical Clinics of North America, vol. 103, no. 2. W.B. Saunders, pp. 263–293, Mar. 01, 2019. doi: 10.1016/j.mcna.2018.10.009.

“2020 Alzheimer’s disease facts and figures,†Alzheimer’s and Dementia, vol. 16, no. 3, pp. 391–460, Mar. 2020, doi: 10.1002/alz.12068.

S. J. Ruiz-Gómez et al., “Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment,†Entropy, vol. 20, no. 1, Jan. 2018, doi: 10.3390/e20010035.

K. Oppedal, K. Engan, T. Eftestøl, M. Beyer, and D. Aarsland, “Classifying Alzheimer’s disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images,†Biomed Signal Process Control, vol. 33, pp. 19–29, Mar. 2017, doi: 10.1016/j.bspc.2016.10.007.

X. Bi and H. Wang, “Early Alzheimer’s disease diagnosis based on EEG spectral images using deep learning,†Neural Networks, vol. 114, pp. 119–135, Jun. 2019, doi: 10.1016/j.neunet.2019.02.005.

E. E. Tülay, B. Güntekin, G. Yener, A. Bayram, C. Başar-Eroğlu, and T. Demiralp, “Evoked and induced EEG oscillations to visual targets reveal a differential pattern of change along the spectrum of cognitive decline in Alzheimer’s Disease,†International Journal of Psychophysiology, vol. 155, pp. 41–48, Sep. 2020, doi: 10.1016/j.ijpsycho.2020.06.001.

G. Fiscon et al., “Combining EEG signal processing with supervised methods for Alzheimer’s patients classification,†BMC Med Inform Decis Mak, vol. 18, no. 1, May 2018, doi: 10.1186/s12911-018-0613-y.

A. Khosla, P. Khandnor, and T. Chand, “A comparative analysis of signal processing and classification methods for different applications based on EEG signals,†Biocybernetics and Biomedical Engineering, vol. 40, no. 2. Elsevier Sp. z o.o., pp. 649–690, Apr. 01, 2020. doi: 10.1016/j.bbe.2020.02.002.

S. Siuly et al., “A New Framework for Automatic Detection of Patients with Mild Cognitive Impairment Using Resting-State EEG Signals,†IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 9, pp. 1966–1976, Sep. 2020, doi: 10.1109/TNSRE.2020.3013429.

S. Simons, P. Espino, and D. Abásolo, “Fuzzy Entropy analysis of the electroencephalogram in patients with Alzheimer’s disease: Is the method superior to Sample Entropy?,†Entropy, vol. 20, no. 1, Jan. 2018, doi: 10.3390/e20010021.

Y. Ding et al., “Fully automated discrimination of Alzheimer’s disease using resting-state electroencephalography signals,†Quant Imaging Med Surg, vol. 12, no. 2, pp. 1063–1078, Feb. 2022, doi: 10.21037/qims-21-430.

P. M. Rodrigues, B. C. Bispo, C. Garrett, D. Alves, J. P. Teixeira, and D. Freitas, “Lacsogram: A New EEG Tool to Diagnose Alzheimer’s Disease,†IEEE J Biomed Health Inform, vol. 25, no. 9, pp. 3384–3395, Sep. 2021, doi: 10.1109/JBHI.2021.3069789.

M. Cejnek, O. Vysata, M. Valis, and I. Bukovsky, “Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG,†Med Biol Eng Comput, vol. 59, no. 11–12, pp. 2287–2296, Nov. 2021, doi: 10.1007/s11517-021-02427-6.

J. P. Amezquita-Sanchez, N. Mammone, F. C. Morabito, S. Marino, and H. Adeli, “A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer’s disease using EEG signals,†J Neurosci Methods, vol. 322, pp. 88–95, Jul. 2019, doi: 10.1016/j.jneumeth.2019.04.013.

M. S. Safi and S. M. M. Safi, “Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters,†Biomed Signal Process Control, vol. 65, Mar. 2021, doi: 10.1016/j.bspc.2020.102338.

J. E. Santos Toural, A. Montoya Pedrón, and E. J. Marañón Reyes, “Classification among healthy, mild cognitive impairment and Alzheimer’s disease subjects based on wavelet entropy and relative beta and theta power,†Pattern Analysis and Applications, vol. 24, no. 2, pp. 413–422, May 2021, doi: 10.1007/s10044-020-00910-8.

V. Bajaj, S. Taran, S. K. Khare, and A. Sengur, “Feature extraction method for classification of alertness and drowsiness states EEG signals,†Applied Acoustics, vol. 163, Jun. 2020, doi: 10.1016/j.apacoust.2020.107224.

D. Puri, S. Nalbalwar, A. Nandgaonkar, P. Kachare, J. Rajput, and A. Wagh, “Alzheimer’s Disease Detection using Empirical Mode Decomposition and Hjorth parameters of EEG signal,†in 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022, 2022, pp. 23–28. doi: 10.1109/DASA54658.2022.9765111.

Y. Miao, M. Zhao, and J. Lin, “Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition,†ISA Trans, vol. 84, pp. 82–95, Jan. 2019, doi: 10.1016/j.isatra.2018.10.008.

N. Ji, L. Ma, H. Dong, and X. Zhang, “EEG signals feature extraction based on DWT and EMD combined with approximate entropy,†Brain Sci, vol. 9, no. 8, Aug. 2019, doi: 10.3390/brainsci9080201.

Z. Huo, Y. Zhang, L. Shu, and M. Gallimore, “A New Bearing Fault Diagnosis Method Based on Fine-to-Coarse Multiscale Permutation Entropy, Laplacian Score and SVM,†IEEE Access, vol. 7, pp. 17050–17066, 2019, doi: 10.1109/ACCESS.2019.2893497.

S. Uddin, A. Khan, M. E. Hossain, and M. A. Moni, “Comparing different supervised machine learning algorithms for disease prediction,†BMC Med Inform Decis Mak, vol. 19, no. 1, Dec. 2019, doi: 10.1186/s12911-019-1004-8.

J. Panyavaraporn and P. Horkaew, “Classification of Alzheimer’s Disease in PET Scans using MFCC and SVM,†vol. 8, no. 5, 2018.

Z. Wang, J. Na, and B. Zheng, “An Improved kNN Classifier for Epilepsy Diagnosis,†IEEE Access, vol. 8, pp. 100022–100030, 2020, doi: 10.1109/ACCESS.2020.2996946.

J. Rabcan, V. Levashenko, E. Zaitseva, and M. Kvassay, “Review of methods for EEG signal classification and development of new fuzzy classification-based approach,†IEEE Access, vol. 8. Institute of Electrical and Electronics Engineers Inc., pp. 189720–189734, 2020. doi: 10.1109/ACCESS.2020.3031447.

H. Sujaini, “Image Classification of Tourist Attractions with K-Nearest Neighbor, Logistic Regression, Random Forest, and Support Vector Machine,†vol. 10, no. 6, 2020.

D. Wang et al., “Classification, experimental assessment, modeling methods and evaluation metrics of Trombe walls,†Renewable and Sustainable Energy Reviews, vol. 124. Elsevier Ltd, May 01, 2020. doi: 10.1016/j.rser.2020.109772.

D. Puri, S. Nalbalwar, A. Nandgaonkar, and A. Wagh, “Alzheimer’s disease detection with Optimal EEG channel selection using Wavelet Transform,†in 2022 International Conference on Decision Aid Sciences and Applications, DASA 2022, 2022, pp. 443–448. doi: 10.1109/DASA54658.2022.9765166.

B. Oltu, M. F. Akşahin, and S. Kibaroğlu, “A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection,†Biomed Signal Process Control, vol. 63, Jan. 2021, doi: 10.1016/j.bspc.2020.102223.

C. Ieracitano, N. Mammone, A. Hussain, and F. C. Morabito, “A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia,†Neural Networks, vol. 123, pp. 176–190, Mar. 2020, doi: 10.1016/j.neunet.2019.12.006.

D. Puri, S. Nalbalwar, A. Nandgaonkar, and A. Wagh, “Alzheimer’s disease detection from optimal electroencephalogram channels and tunable Q-wavelet transform,†Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 3, pp. 1420–1428, Mar. 2022, doi: 10.11591/ijeecs.v25.i3.pp1420-1428.

N. Sharma, M. H. Kolekar, and K. Jha, “Iterative Filtering Decomposition Based Early Dementia Diagnosis Using EEG with Cognitive Tests,†IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 9, pp. 1890–1898, Sep. 2020, doi: 10.1109/TNSRE.2020.3007860.

P. Durongbhan et al., “A dementia classification framework using frequency and time-frequency features based on EEG signals,†IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 5, pp. 826–835, May 2019, doi: 10.1109/TNSRE.2019.2909100.

K. D. Tzimourta et al., “EEG window length evaluation for the detection of Alzheimer’s disease over different brain regions,†Brain Sci, vol. 9, no. 4, Apr. 2019, doi: 10.3390/brainsci9040081.

A. M. Pineda, F. M. Ramos, L. E. Betting, and A. S. L. O. Campanharo, “Quantile graphs for EEG-based diagnosis of Alzheimer’s disease,†PLoS One, vol. 15, no. 6, Jun. 2020, doi: 10.1371/journal.pone.0231169.

L. Tylová, J. Kukal, V. Hubata-Vacek, and O. Vyšata, “Unbiased estimation of permutation entropy in EEG analysis for Alzheimer’s disease classification,†Biomed Signal Process Control, vol. 39, pp. 424–430, Jan. 2018, doi: 10.1016/j.bspc.2017.08.012.

D. Puri, R. Chudiwal, and P. Kachare, Detection of Epilepsy using Wavelet Packet Sub-bands from EEG Signals, vol. 303 SIST. 2022. doi: 10.1007/978-981-19-2719-5_28.

J. S. Rajput, M. Sharma, R. S. Tan, and U. R. Acharya, “Automated detection of severity of hypertension ECG signals using an optimal bi-orthogonal wavelet filter bank,†Comput Biol Med, vol. 123, Aug. 2020, doi: 10.1016/j.compbiomed.2020.103924.

M. Zhu, A. HajiHosseini, T. R. Baumeister, S. Garg, S. Appel-Cresswell, and M. J. McKeown, “Altered EEG alpha and theta oscillations characterize apathy in Parkinson’s disease during incentivized movement,†Neuroimage Clin, vol. 23, Jan. 2019, doi: 10.1016/j.nicl.2019.101922.

D. Puri, R. Ingle, P. Kachare, M. Patil, and R. Awale, “Wavelet Packet Sub-band Based Classification of Alcoholic and Controlled State EEG Signals,†Proceedings of the International Conference on Communication and Signal Processing, Atlantis Press, pp. 562-567, 2017. doi: 10.2991/iccasp-16.2017.82.



  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development