Automated Detection of Alzheimer’s Disease using raw EEG time series via. DWT-CNN model

Yıl 2022, Cilt: 13 Sayı: 4, 673 - 684, 03.01.2023

Mesut Şeker , Mehmet Siraç Özerdem

https://doi.org/10.24012/dumf.1197722

Cited By: 1

Öz

Dementia is an age-related neurological disease and gives rise to profound cognitive decline in patients’ life. Alzheimer’s Disease (AD) is the progression of dementia and AD patients generally have memory loss and behavioral disorders. It is possible to determine the stage of dementia by developing automated systems via. signals obtained from patients. EEG is a popular brain monitoring system due to its cost effective, non-invasive implementation, and higher time resolution. In current study, we include participants of 24 HC (12 eyes open (EO), 12 eyes closed (EC)), and 24 AD (HC (12 eyes open (EO), 12 eyes closed (EC)). The aim of current study is to design a practical AD detection tool for AD/HC participants with a model called DWT-CNN. We performed Discrete Wavelet Transform (DWT) to extract EEG sub-bands. A Conv2D architecture is applied to raw samples of related EEG sub-bands. According to obtained performance metrics calculated from confusion matrices, all AD and HC time series are correctly classified for alpha band and full band range under both EO and EC. Classification rate of AD vs. HC increases under EO state in all cases even if EC is commonly preferred in other studies. We will add MCI patients with equal size and similar demographics and repeat the experimental steps to develop early alert system in future studies. Adding more participants will also increase generalization ability of method. It is also promising study to combine EEG with different modalities (2D TF image conversion, or MRI) in a multimodal approach.

Anahtar Kelimeler

Deep Learning, Alzheimer's Disease, CNN, EEG, Disease Detection

Kaynakça

• [1] S. Yang, J. M. S. Bornot, K. Wong-Lin, and G. Prasad, “M/EEG-Based Bio-Markers to Predict the MCI and Alzheimer’s Disease: A Review from the ML Perspective,” IEEE Trans. Biomed. Eng., vol. 66, no. 10, pp. 2924–2935, 2019.
• [2] martin prince, “World Alzheimer Report,” 2015.
• [3] R. Sivera, H. Delingette, M. Lorenzi, X. Pennec, and N. Ayache, “A model of brain morphological changes related to aging and Alzheimer’s disease from cross-sectional assessments,” Neuroimage, vol. 198, no. December 2018, pp. 255–270, 2019.
• [4] M. A. Parra, S. Butler, W. J. McGeown, L. A. B. Nicholls, and D. J. Robertson, “Globalising strategies to meet global challenges: The case of ageing and dementia,” J. Glob. Health, vol. 9, no. 2, pp. 1–8, 2019.
• [5] L. F. Haas, “Hans Berger (1873-1941), Richard Caton (1842-1926), and electroencephalography.,” J. Neurol. Neurosurg. Psychiatry, vol. 74, no. 1, p. 9, 2003.
• [6] K. D. Tzimourta et al., “Analysis of electroencephalograhic signals complexity regarding Alzheimer’s Disease,” Comput. Electr. Eng., vol. 76, pp. 198–212, 2019.
• [7] A. Farooq, S. Anwar, M. Awais, and M. Alnowami, “Artificial intelligence based smart diagnosis of Alzheimer’s disease and mild cognitive impairment,” 2017 Int. Smart Cities Conf. ISC2 2017, pp. 0–3, 2017.
• [8] 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, 2019.
• [9] D. Kim and K. Kim, “Detection of Early Stage Alzheimer’s Disease using EEG Relative Power with Deep Neural Network,” in 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018, pp. 352–355.
• [10] C. Ieracitano, N. Mammone, A. Bramanti, A. Hussain, and F. C. Morabito, “A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings,” Neurocomputing, vol. 323, pp. 96–107, 2019.
• [11] Y. Zhao and L. He, “Deep Learning in the EEG Diagnosis of Alzheimer’s Disease,” in ACCV Workshops, 2014.
• [12] C. J. Huggins 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., vol. 18, no. 4, 2021.
• [13] A. M. Alvi, S. Siuly, H. Wang, K. Wang, and F. Whittaker, “A deep learning based framework for diagnosis of mild cognitive impairment,” Knowledge-Based Syst., vol. 248, p. 108815, 2022.
• [14] A. M. Pineda, F. M. Ramos, L. E. Betting, S. L. Andriana, and O. C. Id, “Quantile graphs for EEG-based diagnosis of Alzheimer ’ s disease,” PLoS One, vol. 15, no. 6, pp. 1–15, 2020.
• [15] L. Xie, C. Lu, Z. Liu, L. Yan, and T. Xu, “Studying critical frequency bands and channels for EEG-based automobile sound recognition with machine learning,” Appl. Acoust., vol. 185, p. 108389, 2022.
• [16] C. Ieracitano, N. Mammone, A. Bramanti, A. Hussain, and F. C. Morabito, “A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings,” Neurocomputing, vol. 323, pp. 96–107, 2019.
• [17] D. P. X. Kan, P. E. Croarkin, C. K. Phang, and P. F. Lee, “EEG Differences Between Eyes-Closed and Eyes-Open Conditions at the Resting Stage for Euthymic Participants,” Neurophysiology, vol. 49, no. 6, pp. 432–440, 2017.
• [18] F. Miraglia, F. Vecchio, P. Bramanti, and P. M. Rossini, “EEG characteristics in ‘eyes-open’ versus ‘eyes-closed’ conditions: Small-world network architecture in healthy aging and age-related brain degeneration,” Clin. Neurophysiol., vol. 127, no. 2, pp. 1261–1268, 2016.
• [19] P. A. M. Kanda, E. F. Oliveira, and F. J. Fraga, “EEG epochs with less alpha rhythm improve discrimination of mild Alzheimer’s,” Comput. Methods Programs Biomed., vol. 138, pp. 13–22, 2017.
• [20] M. de Bardeci, C. T. Ip, and S. Olbrich, “Deep learning applied to electroencephalogram data in mental disorders: A systematic review,” Biol. Psychol., vol. 162, p. 108117, 2021.