How to Cite
Publication Dates
Abstract
The purpose of the current investigation is to distinguish between working memory ( ) in five patients with vascular dementia ( ), fifteen post-stroke patients with mild cognitive impairment ( ), and fifteen healthy control individuals ( ) based on background electroencephalography (EEG) activity. The elimination of EEG artifacts using wavelet (WT) pre-processing denoising is demonstrated in this study. In the current study, spectral entropy ( ), permutation entropy ( ), and approximation entropy ( ) were all explored. To improve the classification using the k-nearest neighbors ( NN) classifier scheme, a comparative study of using fuzzy neighbourhood preserving analysis with -decomposition ( ) as a dimensionality reduction technique and the improved binary gravitation search ( ) optimization algorithm as a channel selection method has been conducted. The NN classification accuracy was increased from 86.67% to 88.09% and 90.52% using the dimensionality reduction technique and the channel selection algorithm, respectively. According to the findings, reliably enhances discrimination of , , and participants. Therefore, WT, entropy features, IBGSA and NN classifiers provide a valid dementia index for looking at EEG background activity in patients with and .
References
B. C. Campbell, D. A. De Silva, M. R. Macleod, S. B. Coutts, L. H. Schwamm, S. M. Davis, et al., "Ischaemic stroke," Nature Reviews Disease Primers, vol. 5, p. 70, 2019.
N. K. Al-Qazzaz, S. H. Ali, S. A. Ahmad, and S. Islam, "Cognitive assessments for the early diagnosis of dementia after stroke," Neuropsychiatric disease and treatment, vol. 10, p. 1743, 2014.
N. K. Al-Qazzaz, S. H. Ali, S. A. Ahmad, S. Islam, and K. Mohamad, "Cognitive impairment and memory dysfunction after a stroke diagnosis: a post-stroke memory assessment," Neuropsychiatric disease and treatment, vol. 10, p. 1677, 2014.
C. N. Kan, J. Cano, X. Zhao, Z. Ismail, C. L.-H. Chen, and X. Xu, "Prevalence, clinical correlates, cognitive trajectories, and dementia risk associated with mild behavioral impairment in Asians," The Journal of Clinical Psychiatry, vol. 83, p. 40123, 2022.
N. K. Al-Qazzaz, S. H. B. M. Ali, S. A. Ahmad, M. S. Islam, and J. Escudero, "Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis," Medical & biological engineering & computing, vol. 56, pp. 137-157, 2018.
N. K. Al-Qazzaz, S. H. B. Ali, S. A. Ahmad, K. Chellappan, M. S. Islam, and J. Escudero, "Role of EEG as Biomarker in the Early Detection and Classification of Dementia," The Scientific World Journal, vol. 2014, 2014.
E. H. Houssein, A. Hammad, and A. A. Ali, "Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review," Neural Computing and Applications, vol. 34, pp. 12527-12557, 2022.
N. K. Al-Qazzaz, S. Hamid Bin Mohd Ali, S. A. Ahmad, M. S. Islam, and J. Escudero, "Automatic artifact removal in EEG of normal and demented individuals using ICA–WT during working memory tasks," Sensors, vol. 17, p. 1326, 2017.
M. Kashefpoor, H. Rabbani, and M. Barekatain, "Automatic diagnosis of mild cognitive impairment using electroencephalogram spectral features," Journal of medical signals and sensors, vol. 6, p. 25, 2016.
N. Sharma, M. Kolekar, K. Jha, and Y. Kumar, "EEG and cognitive biomarkers based mild cognitive impairment diagnosis," Irbm, vol. 40, pp. 113-121, 2019.
P. Durongbhan, Y. Zhao, L. Chen, P. Zis, M. De Marco, Z. C. Unwin, 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, pp. 826-835, 2019.
A. H. H. Al-Nuaimi, E. Jammeh, L. Sun, and E. Ifeachor, "Complexity measures for quantifying changes in electroencephalogram in Alzheimer’s disease," Complexity, vol. 2018, 2018.
N. K. Al-Qazzaz, M. K. Sabir, A. H. Al-Timemy, and K. Grammer, "An integrated entropy-spatial framework for automatic gender recognition enhancement of emotion-based EEGs," Medical & Biological Engineering & Computing, pp. 1-20, 2022.
M. Şeker, Y. Özbek, G. Yener, and M. S. Özerdem, "Complexity of EEG dynamics for early diagnosis of Alzheimer's disease using permutation entropy neuromarker," Computer Methods and Programs in Biomedicine, vol. 206, p. 106116, 2021.
S. J. Ruiz-Gómez, C. Gómez, J. Poza, G. C. Gutiérrez-Tobal, M. A. Tola-Arribas, M. Cano, et al., "Automated multiclass classification of spontaneous EEG activity in Alzheimer’s disease and mild cognitive impairment," Entropy, vol. 20, p. 35, 2018.
C. S. Musaeus, K. Engedal, P. Høgh, V. Jelic, M. Mørup, M. Naik, et al., "EEG theta power is an early marker of cognitive decline in dementia due to Alzheimer’s disease," Journal of Alzheimer's Disease, vol. 64, pp. 1359-1371, 2018.
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, pp. 413-422, 2021.
B. Oltu, M. F. Akşahin, and S. Kibaroğlu, "A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection," Biomedical Signal Processing and Control, vol. 63, p. 102223, 2021.
M. KavitaMahajan and M. S. M. Rajput, "A Comparative study of ANN and SVM for EEG Classification," International Journal of Engineering, vol. 1, 2012.
N. K. Al-Qazzaz, S. H. M. Ali, and S. A. Ahmad, "Differential Evolution Based Channel Selection Algorithm on EEG Signal for Early Detection of Vascular Dementia among Stroke Survivors," in 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2018, pp. 239-244.
T. Lan, D. Erdogmus, A. Adami, S. Mathan, and M. Pavel, "Channel selection and feature projection for cognitive load estimation using ambulatory EEG," Computational intelligence and neuroscience, vol. 2007, pp. 8-8, 2007.
M. Arvaneh, C. Guan, K. K. Ang, and C. Quek, "Optimizing the channel selection and classification accuracy in EEG-based BCI," IEEE Transactions on Biomedical Engineering, vol. 58, pp. 1865-1873, 2011.
M. Schröder, T. N. Lal, T. Hinterberger, M. Bogdan, N. J. Hill, N. Birbaumer, et al., "Robust EEG channel selection across subjects for brain-computer interfaces," EURASIP Journal on Applied Signal Processing, vol. 2005, pp. 3103-3112, 2005.
N. K. Al-Qazzaz, M. K. Sabir, S. Ali, S. A. Ahmad, and K. Grammer, "Effective EEG Channels for Emotion Identification over the Brain Regions using Differential Evolution Algorithm," in 2019 41th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019.
N. K. Al-Qazzaz, S. H. B. M. Ali, S. A. Ahmad, and J. Escudero, "Optimal EEG Channel Selection for Vascular Dementia Identification Using Improved Binary Gravitation Search Algorithm," in International Conference for Innovation in Biomedical Engineering and Life Sciences, 2017, pp. 125-130.
R. N. Khushaba, S. Kodagoda, D. Liu, and G. Dissanayake, "Electromyogram (EMG) based fingers movement recognition using neighborhood preserving analysis with QR-decomposition," in Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2011 Seventh International Conference on, 2011, pp. 1-105.
N. K. Al-Qazzaz, S. Ali, S. A. Ahmad, and J. Escudero, "Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition," in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017, pp. 3174-3177.
N. K. Al-Qazzaz, S. Ali, S. Islam, S. Ahmad, and J. Escudero, "EEG Wavelet Spectral Analysis During a Working Memory Tasks in Stroke-Related Mild Cognitive Impairment Patients," in International Conference for Innovation in Biomedical Engineering and Life Sciences, 2016, pp. 82-85.
Z. German-Sallo and C. Ciufudean, "Waveform-adapted wavelet denoising of ECG signals."
A. Shoeb and G. Cliord, "Chapter 16 - Wavelets; Multiscale Activity in Physiological Signals," in Biomedical Signal and Image Processing, ed, 2005.
J. Escudero, R. Hornero, D. Abásolo, and A. Fernández, "Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease," Medical engineering & physics, vol. 31, pp. 872-879, 2009.
J. Escudero Rodríguez, "Applications of blind source separation to the magnetoencephalogram background activity in alzheimer's disease," Universidad de Valladolid, 2010.
R. Hornero, J. Escudero, A. Fernández, J. Poza, and C. Gómez, "Spectral and nonlinear analyses of MEG background activity in patients with Alzheimer's disease," Biomedical Engineering, IEEE Transactions on, vol. 55, pp. 1658-1665, 2008.
J. S. Richman and J. R. Moorman, "Physiological time-series analysis using approximate entropy and sample entropy," American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, pp. H2039-H2049, 2000.
D. E. Lake, J. S. Richman, M. P. Griffin, and J. R. Moorman, "Sample entropy analysis of neonatal heart rate variability," American Journal of Physiology-Regulatory, Integrative and Comparative Physiology, vol. 283, pp. R789-R797, 2002.
H. Azami and J. Escudero, "Improved multiscale permutation entropy for biomedical signal analysis: Interpretation and application to electroencephalogram recordings," Biomedical Signal Processing and Control, vol. 23, pp. 28-41, 2016.
A. Holzinger, M. Hörtenhuber, C. Mayer, M. Bachler, S. Wassertheurer, A. J. Pinho, et al., "On entropy-based data mining," in Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, ed: Springer, 2014, pp. 209-226.
F. C. Morabito, D. Labate, F. La Foresta, A. Bramanti, G. Morabito, and I. Palamara, "Multivariate multi-scale permutation entropy for complexity analysis of Alzheimer’s disease EEG," Entropy, vol. 14, pp. 1186-1202, 2012.
E. Ferlazzo, N. Mammone, V. Cianci, S. Gasparini, A. Gambardella, A. Labate, et al., "Permutation entropy of scalp EEG: A tool to investigate epilepsies: Suggestions from absence epilepsies," Clinical Neurophysiology, vol. 125, pp. 13-20, 2014.
C. Bandt and B. Pompe, "Permutation entropy: a natural complexity measure for time series," Physical review letters, vol. 88, p. 174102, 2002.
M. Zanin, L. Zunino, O. A. Rosso, and D. Papo, "Permutation entropy and its main biomedical and econophysics applications: a review," Entropy, vol. 14, pp. 1553-1577, 2012.
N. K. Al-Qazzaz, M. K. Sabir, S. H. B. M. Ali, S. A. Ahmad, and K. Grammer, "Multichannel optimization with hybrid spectral-entropy markers for gender identification enhancement of emotional-based EEGs," IEEE Access, vol. 9, pp. 107059-107078, 2021.
A. Ghaemi, E. Rashedi, A. M. Pourrahimi, M. Kamandar, and F. Rahdari, "Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm," Biomedical Signal Processing and Control, vol. 33, pp. 109-118, 2017.
J. Xiang, X. Han, F. Duan, Y. Qiang, X. Xiong, Y. Lan, et al., "A novel hybrid system for feature selection based on an improved gravitational search algorithm and k-NN method," Applied Soft Computing, vol. 31, pp. 293-307, 2015.
N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: synthetic minority over-sampling technique," Journal of artificial intelligence research, pp. 321-357, 2002.
R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Ijcai, 1995, pp. 1137-1145.
Y. Song and J. Zhang, "Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine," Journal of neuroscience methods, vol. 257, pp. 45-54, 2016.
I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques: Morgan Kaufmann, 2005.
M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: an update," ACM SIGKDD explorations newsletter, vol. 11, pp. 10-18, 2009.
L. A. Moctezuma and M. Molinas, "Multi-objective optimization for EEG channel selection and accurate intruder detection in an EEG-based subject identification system," Scientific Reports, vol. 10, pp. 1-12, 2020.
Copyright: Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.