Identification of Alzheimer’s disease from central lobe EEG signals utilizing machine learning and residual neural network

Bairagi, 2018, EEG signal analysis for early diagnosis of Alzheimer's disease using spectral and wavelet-based features, Int. J. Inf. Technol., 10, 403 Trambaiolli, 2017, Feature selection before EEG classification supports the diagnosis of Alzheimer's disease, Clin. Neurophysiol., 128, 2058, 10.1016/j.clinph.2017.06.251 Blank, 2019, Alzheimer's Disease and Other Dementias: An Introduction, 1 Orhan, 2011, EEG signals classification using the K-means clustering and a multilayer perceptron neural network model, Expert Syst. Appl., 38, 13475, 10.1016/j.eswa.2011.04.149 Hjorth, 1970, EEG analysis based on time domain properties, Electroencephalogr. Clin. Neurophysiol., 29, 306, 10.1016/0013-4694(70)90143-4 Buyrukoğlu, 2022, Correlation value determined to increase Salmonella prediction success of deep neural network for agricultural waters, Environ. Monit. Assessment, 194, 1, 10.1007/s10661-022-10050-7 Buyrukoğlu, 2022, Stacked-based ensemble machine learning model for positioning footballer, Arab. J. Sci. Eng., 1–13 Halde, 2016, Application of Machine Learning algorithms for betterment in education system, 1110 Kulkarni, 2017, Extracting salient features for EEG-based diagnosis of Alzheimer's disease using support vector machine classifier, IETE J. Res., 63, 11, 10.1080/03772063.2016.1241164 Ruiz-Gómez, 2018, Automated multiclass classification of spontaneous EEG activity in Alzheimer's disease and mild cognitive impairment, Entropy, 20, 35, 10.3390/e20010035 Amezquita-Sanchez, 2019, A novel methodology for automated differential diagnosis of mild cognitive impairment and the Alzheimer's disease using EEG signals, J. Neurosci. Methods, 322, 88, 10.1016/j.jneumeth.2019.04.013 Kulkarni, 2018, Use of complexity-based features in diagnosis of mild Alzheimer's disease using EEG signals, Int. J. Inf. Technol., 10, 59 Tzimourta, 2019, EEG window length evaluation for the detection of Alzheimer's disease over different brain regions, Brain Sci., 9, 81, 10.3390/brainsci9040081 Buyrukoğlu, 2021, Early detection of alzheimer's disease using data mining: comparision of ensemble feature selection approaches, Konya J. Eng. Sci., 9, 50, 10.36306/konjes.731624 Buyrukoğlu, 2021, Improvement of Machine Learning Models' Performances based on Ensemble Learning for the detection of Alzheimer's Disease, 102 Yu, 2020, Supervised network-based fuzzy learning of EEG signals for Alzheimer's disease identification, IEEE Trans. Fuzzy Syst., 28, 60, 10.1109/TFUZZ.2019.2903753 Digambar V. Puri, Sanjay L. Nalbalwar, Anil B. Nandgaonkar, Jayanand P. Gawande, Abhay Wagh, Automatic detection of Alzheimer’s disease from EEG signals using low-complexity orthogonal wavelet filter banks, Biomedical Signal Processing and Control, Volume 81, 2023, 104439, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2022.104439. Yu, 2018, Modulation of spectral power and functional connectivity in human brain by acupuncture stimulation, IEEE Trans. Neural Syst. Rehabil. Eng., 26, 977, 10.1109/TNSRE.2018.2828143 Yu, 2019, Modulation effect of acupuncture on functional brain networks and classification of its manipulation with EEG signals, IEEE Trans. Neural Syst. Rehabil. Eng., 27, 1973, 10.1109/TNSRE.2019.2939655 Pini, 2021, Breakdown of specific functional brain networks in clinical variants of Alzheimer's disease, Ageing Res Rev., 72, 10.1016/j.arr.2021.101482 Alzheimer's Disease International & McGill University. World Alzheimer Report 2021, 2021. Pineda, 2020, Quantile graphs for EEG-based diagnosis of Alzheimer's disease, PLoS One, 15, e0231169, 10.1371/journal.pone.0231169 https://figshare.com/articles/dataset/dataset_zip/5450293/1. Bakshi, 1998, Multiscale PCA with application to multivariate statistical process monitoring, AIChE J., 44, 1596, 10.1002/aic.690440712 P. Jahankhani, V. Kodogiannis, K. Revett, EEG signal classification using wavelet feature extraction and neural networks, in: IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA’06), 2006, pp. 120–124. Amin, 2015, Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques, Australas. Phys. Eng. Sci. Med., 38, 139, 10.1007/s13246-015-0333-x Gonzalez, 2009 R.O. Duda, P.E. Hart, D.G. Stork, Pattern Recognition: second edition WILEY-INTERSCIENCE, 2001. Fukunaga, 1990 C.J.C. Burges, A tutorial on support vector machines for pattern recognition: Knowledge Discovery and Data Mining 2 (1998) 121. K. P. Bennett and C. Campbell (2000) Support vector machines: Type Explorations Newslette, 2:1. B. Blankertz, M. Kawanabe, R. Tomioka, F. Hohlefeld, K.-r. Müller, V. V. Nikulin, Invariant common spatial patterns: Alleviating nonstationarities in brain-computer interfacing: in Advances in neural information processing systems (2007) 113–120. Garrett, 2003, Comparison of linear, nonlinear, and feature selection methods for EEG signal classification, IEEE Trans. Neural Syst. Rehabilitation Eng., 11, 141, 10.1109/TNSRE.2003.814441 Rakotomamonjy, 2005, Ensemble of SVMs for improving brain computer interface P300 speller performances Rakotomamonjy, 2008, BCI competition III: Dataset II - ensemble of SVMs for BCI P300 speller, IEEE Trans Biomed. Eng., 55, 1147, 10.1109/TBME.2008.915728 Fouad, 2022, Role of deep learning in improving the performance of driver fatigue alert system, Traitement du Signal, 39, 577, 10.18280/ts.390219 Fouad, 2022, A robust and efficient EEG-based drowsiness detection system using different machine learning algorithms, Ain Shams Eng. J., 1 Jain, 2000, Statistical pattern recognition: a review, IEEE Trans. Pattern Anal. Mach. Intell., 22, 4, 10.1109/34.824819 T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference and Prediction“. s.l. : Springer, 2008. Manning, 2008 Olson M. Essays on random forest ensembles. Ph.D. Thesis. 3420 Walnut St., Philadelphia, PA 19104‐6206; 2018. J.N. Morgan, J.A. ve Sonquist, Problems in the analysis of survey data, and a proposal, J. Amer. Statist. Ass. 58 (1963) 415-434. L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone), Classification and Regression Trees. Wadsworth International, Belmont, CA, 1984. Breiman, 1996, Bagging predictors, Mach. Learn., 26, 123, 10.1007/BF00058655 Ho, 1998, The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal Mach Intell, 20, 832, 10.1109/34.709601 Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 Dekking, 2005, 181 Hosmer, 2000 R. Pearl, L.J. Reed, J.F. K, The logistic curve and the consensus count of 1940, Science, 14 (1940) 895:901. G. Yangın, Xgboost ve Karar Ağacı Tabanlı Algoritmaların Diyabet Veri Setleri Üzerine Uygulaması, Yüksek Lisans Tezi, Mimar Sinan Güzel Sanatlar Üniversitesi Fen Bilimleri Enstitüsü, İstanbul, 2019. Peng, 2002, An introduction to logistic regression analysis and reporting, J. Educ. Res., 96, 3, 10.1080/00220670209598786 S. Baş, A. Uzun, Tedarik Zincirinde Müşteri Siparişlerinin Lojistik Regresyon Analizi İle Değerlendirilmesi, Ömer Halis Demir Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 11(3) (2018) 67-81. Hewett, 2019, Systematic selection of key logistic regression variables for risk prediction analyses: a five-factor maximum model, Clin. J. Sport Med. Off. J. Can. Acad. Sport Med., 29, 78, 10.1097/JSM.0000000000000486 Felitti, 1998, Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Adverse Childhood Experiences (ACE) Study, Am. J. Prev. Med., 14, 245, 10.1016/S0749-3797(98)00017-8 Cranmer, 2015, Kantian fractionalization predicts the conflict propensity of the international system, Proc. Natl. Acad. Sci., 112, 11812, 10.1073/pnas.1509423112 S. Vijayarani, S. Dhayanand, M.P. Research Scholar, Data Mınıng Classıfıcatıon Algorıthms for Kıdney Dısease Predıctıon, Int. J. Cybern. Informatics 4(4) (2015) 13–25, 2015. Hebb, 2005 Deng, 2014, Deep learning: methods and applications. Foundations and Trends®, Signal Process., 7, 197 Makantasis, 2015, deeply supervised learning for hyperspectral data classification through convolutional neural networks, 4959 A. Radford, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial networks, 2015. arXiv preprint arXiv:1511.06434. I. Higgins, L. Matthey, X. Glorot, A. Pal, B. Uria, C. Blundell, et al., 2016. early visual concept learning with unsupervised deep learning. arXiv preprint arXiv:1606.05579. N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, K. Talwar, semi-supervised Knowledge transfer for deep learning from private training data, 2016. arXiv preprint arXiv:1610.05755. L. Liu, C. Shen, A. van den Hengel, The treasure beneath convolutional layers: cross- convolutional-layer pooling for image classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, p. 4749-4757. Deng, Jia, et al., Imagenet: A large-scale hierarchical image database, Comput. Vis. Patt. Recognit., 2009. CVPR 2009. IEEE Conference on. IEEE, 2009. Hsu, 2016 Fouad, 2020, Improving the performance of P300 BCI system using different methods, Netw Model Anal Health Inform Bioinforma, 9 Labib, 2020, Multiple classification techniques toward a robust and reliable P300 BCI system, Biomed Eng Appl Basis Commun., 32, 2050010, 10.4015/S1016237220500106 W. H. Organization and others, Dementia: a public health priority. World Health Organization, 2012. Jeong, 2004, EEG dynamics in patients with Alzheimer's disease, Clin. Neurophysiol., 115, 1490, 10.1016/j.clinph.2004.01.001 C. Patterson and others, “World alzheimer report 2018,” 2018. Dauwels, 2010, Diagnosis of Alzheimer's disease from EEG signals: where are we standing?, Curr. Alzheimer Res., 7, 487, 10.2174/156720510792231720 Alberdi, 2016, On the early diagnosis of Alzheimer's disease from multimodal signals: a survey, Artif. Intel. Med., 71, 1, 10.1016/j.artmed.2016.06.003 Cassani, 2018, Systematic review on resting-state EEG for Alzheimer's disease diagnosis and progression assessment, Dis. Markers, 2018, 10.1155/2018/5174815