Early detection of Alzheimer's disease: A comprehensive study on Alzheimer's disease early detection A comprehensive study on Alzheimer's disease early detection
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ABSTRACT: Alzheimer's disease (AD), a chronic neurological disorder, is an important reason of dementia, mostly among older adults. An early and accurate diagnosis of AD is necessary for timely detection and treatment. This survey review the developments the use in deep learning (DL) and machine learning (ML) methods for AD classification and detection. Focused brain network analysis to identify alterations in local and global connectivity and spectral and coherence investigations of EEG data to identify disruptions associated with AD are two research strategies that have highlighted the mechanisms behind AD. Two examples of robust deep learning models that have achieved notable precision in identifying AD using neuroimaging datasets like MRI and EEG are convolutional neural networks (CNNs) and ensemble learning. Using sleep EEG to detect mild cognitive impairment (MCI), research has demonstrated the utility of functional connectivity metrics. Furthermore, hybrid models that merge CNNs and ensemble learning have shown promise in both feature extraction and classification. Latest inventions , like gated graph convolutional networks for working with non-Euclidean data and transformer-based speech recognition models, have taken the explainable and multimodal diagnostic frameworks to the next level. This study review and gives summarize knowledge about techniques and methods used to detect Alzheimer , furthermore limitations of work and search guide.
Keywords: Electroencephalogram (EEG), mild cognitive impairment (MCI), and Alzheimer's disease (AD) Power Spectral Density (PSD)
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Pakistan Journal Emerging Science and Technologies (PJEST) in collaboration with Govt. Islamia Graduate College Civil Lines Lahore, Pakistan is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
who, o. (2023). Overweight, available online: Https://www. Who. Int/en/news-room/fact- sheets/detail/obesity-and-overweight.
Alzheimer’s disease international. (2021). New data predicts the number of people living with alzheimer’s disease to triple by 2050. Https://www.alzint.org.
R. Wang, J. Wang, H. Yu, X. Wei, C. Yang, and B. Deng, “Power spectral density and coherence analysis of Alzheimer’s EEG,” Cognitive Neurodynamics, vol. 9, no. 3, pp. 291–304, 2014. doi: 10.1007/s11571-014-9325-x
S. Afshari and M. Jalili, “Directed functional networks in Alzheimer’s disease: Disruption of global and local connectivity measures,” IEEE J. Biomed. Health Inform., vol. 21, no. 4, pp. 949–955, 2017, doi: 10.1109/JBHI.2016.2578954.
K. A. I. Aboalayon, H. T. Ocbagabir, and M. Faezipour, “Efficient sleep stage classification based on EEG signals,” in Proc. IEEE Long Island Systems, Applications and Technology Conf. (LISAT), 2014, pp. 1–6, doi: 10.1109/LISAT.2014.6961341.
D. Geng, C. Wang, Z. Fu, Y. Zhang, K. Yang, and H. An, “Sleep EEG-based approach to detect mild cognitive impairment,” Front. Aging Neurosci., vol. 14, 2022, doi: 10.3389/fnagi.2022.1023861.
A. D’Atri et al., “EEG alterations during wake and sleep in mild cognitive impairment and Alzheimer’s disease,” iScience, vol. 24, no. 4, p. 102386, 2021, doi: 10.1016/j.isci.2021.102386.
D. Klepl, F. He, M. Wu, D. J. Blackburn, and P. Sarrigiannis, “Adaptive gated graph convolutional network for explainable diagnosis of Alzheimer’s disease using EEG data,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 31, pp. 3978–3987, 2023, doi: 10.1109/TNSRE.2023.3278823.
Z.-J. Lin, Y.-J. Chen, P.-C. Kuo, L. Huang, C.-J. Hu, and C.-Y. Chen, “Dementia assessment using Mandarin speech with an attention-based speech recognition encoder,” Proc. ICASSP, 2023, doi: 10.1109/ICASSP49357.2023.10096519.
Y. Qin et al., “Directed brain network analysis for fatigue driving based on EEG source signals,” Entropy, vol. 24, no. 8, p. 1093, 2022, doi: 10.3390/e24081093.
H. Huang et al., “EEG-based sleep staging analysis with functional connectivity,” Sensors, vol. 21, no. 6, p. 1988, 2021, doi: 10.3390/s21061988.
L. Ilias and D. Askounis, “Explainable identification of dementia from transcripts using transformer networks,” IEEE J. Biomed. Health Inform., vol. 26, no. 8, pp. 4153–4164, 2022, doi: 10.1109/JBHI.2022.3145120.
Z. S. Syed, M. S. S. Syed, M. Lech, and E. Pirogova, “Automated recognition of Alzheimer’s dementia using bag-of-deep-features and model ensembling,” IEEE Access, vol. 9, pp. 88377–88390, 2021, doi: 10.1109/ACCESS.2021.3080070.
Y. Zhu, X. Liang, J. A. Batsis, and R. M. Roth, “Exploring deep transfer learning techniques for Alzheimer’s dementia detection,” Front. Comput. Sci., vol. 3, p. 715420, 2021, doi: 10.3389/fcomp.2021.715420.
J. Zuo, K. Zeitouni, and Y. Taher, “SMATE: Semi-supervised spatiotemporal representation learning on multivariate time series,” in Proc. IEEE Int. Conf. Data Mining (ICDM), 2021, pp. 1565–1570, doi: 10.1109/ICDM51609.2021.00215. S. Khalighi, T. Sousa, J. M. Santos, and U. Nunes, “ISRUC-sleep: A comprehensive public dataset for sleep researchers,” Computer Methods and Programs in Biomedicine, vol. 124, pp. 180–192, 2016.
A. Guillot, F. Sauvet, E. During, and V. Thorey, “Dreem open datasets: Multi-scored sleep datasets to compare human and automated sleep staging,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. PP, pp. 1–1, 2020. doi:10.1016/j.cmpb.2015.10.013
Y. Wang, J. Deng, T. Wang, B. Zheng, S. Hu, X. Liu, and H. Meng, “Exploiting prompt learning with pre-trained language models for Alzheimer’s disease detection,” in ICASSP 2023 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023. doi:10.1109/TNSRE.2020.3011181.
M. Y. Avci et al., “Unsupervised analysis of Alzheimer’s disease signatures using 3D deformable autoencoders,” arXiv preprint arXiv:2407.03863, 2024. doi:10.1109/JBHI.2022.3145120
Z. Breijyeh and R. Karaman, “Comprehensive review on Alzheimer’s disease: Causes and treatment,” Molecules, vol. 25, no. 24, p. 5789, 2020. doi:10.1109/ACCESS.2021.3080070.
Y. Zhang, Q. Teng, Y. Liu, Y. Liu, and X. He, “Diagnosis of Alzheimer’s disease based on regional attention with sMRI gray matter slices,” Journal of Neuroscience Methods, vol. 365, p. 109376, 2022.,doi:10.3389/fcomp.2021.624683.
S. T. Goenka, “AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer’s disease through multiple neuroimaging computational approaches,” Biomedical Signal Processing and Control, vol. 74, p. 103500, 2022, doi:10.1109/ICDM51609.2021.00215.
S. T. Goenka, “Deep learning for Alzheimer prediction using brain biomarkers,” Artificial Intelligence Review, vol. 54, pp. 4827–4871, 2021, doi:10.3390/e24081093
N. Rahim, S. El-Sappagh, S. Ali, K. Muhammad, J. D. Ser, and T. Abuhmed, “Prediction of Alzheimer’s progression based on multimodal deep-learning-based fusion and visual explainability of time-series data,” Information Fusion, vol. 92, pp. 363–388, 2023, doi:10.3390/s21061988.
S. Fouladi, A. A. Safaei, N. I. Arshad, M. J. Ebadi, and A. Ahmadian, “The use of artificial neural networks to diagnose Alzheimer’s disease from brain images,” Multimedia Tools and Applications, vol. 81, pp. 37681–37721, 2022.
Alroobaea, R., Mechti, S., Haoues, M., Rubaiee, S., Ahmed, A., Andejany, M., ... & Sengan, S. (2021). Alzheimer's disease early detection using machine learning techniques. A. Mehmood et al., “Early diagnosis of Alzheimer's disease based on convolutional neural networks,” Computer Systems Science & Engineering, vol. 43, no. 1, 2022.
Arafa, D. A., Moustafa, H. E. D., Ali, H. A., Ali-Eldin, A. M., & Saraya, S. F. (2024). A deep learning framework for early diagnosis of Alzheimer’s disease on MRI images. Multimedia Tools and Applications, 83(2), 3767-3799.
Basaia, S., Agosta, F., Wagner, L., Canu, E., Magnani, G., Santangelo, R., ... & Alzheimer's Disease Neuroimaging Initiative. (2019). Automated classification of Alzheimer's disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage: Clinical, 21, 101645.
Pan, D., Zeng, A., Jia, L., Huang, Y., Frizzell, T., & Song, X. (2020). Early detection of Alzheimer’s disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning. Frontiers in neuroscience, 14, 259.
H. A. Helaly, M. Badawy, and A. Y. Haikal, “Deep learning approach for early detection of Alzheimer’s disease,” Cognitive Computation, vol. 14, no. 5, pp. 1711–1727, 2022.
Liu, S., Masurkar, A. V., Rusinek, H., Chen, J., Zhang, B., Zhu, W., ... & Razavian, N. (2022). Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs. Scientific reports, 12(1), 17106.
Shahwar, T., Zafar, J., Almogren, A., Zafar, H., Rehman, A. U., Shafiq, M., & Hamam, H. (2022). Automated detection of Alzheimer’s via hybrid classical quantum neural networks. Electronics, 11(5), 721
Hazarika, R. A., Abraham, A., Kandar, D., & Maji, A. K. (2021). An improved LeNet-deep neural network model for Alzheimer’s disease classification using brain magnetic resonance images. IEEE Access, 9, 161194-161207.
M. W. Ashraf, S. Tayyaba, and N. Afzulpurkar, "Micro electromechanical systems (MEMS) based microfluidic devices for biomedical applications," International journal of molecular sciences, vol. 12, no. 6, pp. 3648-3704, 2011. https://doi.org/10.3390/ijms12063648
E. Stemme and G. Stemme, "A valveless diffuser/nozzle-based fluid pump," Sensors and Actuators A: physical, vol. 39, no. 2, pp. 159-167, 1993. https://doi.org/10.1016/0924-4247(93)80213-Z
M. J. Afzal, S. Tayyaba, M. W. Ashraf, M. K. Hossain, M. J. Uddin, and N. Afzulpurkar, "Simulation, fabrication and analysis of silver based ascending sinusoidal microchannel (ASMC) for implant of varicose veins," Micromachines, vol. 8, no. 9, p. 278, 2017. https://doi.org/10.3390/mi8090278
M. J. Afzal, M. W. Ashraf, S. Tayyaba, M. K. Hossain, and N. Afzulpurkar, "Sinusoidal Microchannel with Descending Curves for Varicose Veins Implantation," Micromachines, vol. 9, no. 2, p. 59, 2018. https://doi.org/10.3390/mi9020059
S. Tayyaba, M. W. Ashraf, Z. Ahmad, N. Wang, M. J. Afzal, and N. Afzulpurkar, "Fabrication and Analysis of Polydimethylsiloxane (PDMS) Microchannels for Biomedical Application," Processes, vol. 9, no. 1, p. 57, 2021. https://doi.org/10.3390/pr9010057