STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network

Al-Shourbaji I, Kachare PH, Abualigah L, Abdelhag ME, Elnaim B, Anter AM, Gandomi AH (2023) A deep batch normalized convolution approach for improving COVID-19 detection from chest X-ray images. Pathogens. https://doi.org/10.3390/pathogens12010017

Article  Google Scholar 

Alvi AM, Siuly S, Wang H (2022) A long short-term memory based framework for early detection of mild cognitive impairment from EEG signals. IEEE Trans Emerging Top Comput Intell. https://doi.org/10.1109/TETCI.2022.3186180

Article  Google Scholar 

Aslam MS, Radhika T, Chandrasekar A, Zhu Q (2024) Improved event-triggered-based output tracking for a class of delayed networked t–s fuzzy systems. Int J Fuzzy Syst 1–14

Atri A (2019) The Alzheimer’s disease clinical spectrum: diagnosis and management. Med Clin N Am 103(2):263–293. https://doi.org/10.1016/j.mcna.2018.10.009. (Neurology for the Non-Neurologist)

Article  PubMed  Google Scholar 

Aydın S (2022) Cross-validated adaboost classification of emotion regulation strategies identified by spectral coherence in resting-state. Neuroinformatics 20(3):627–639. https://doi.org/10.1007/s12021-021-09542-7

Article  PubMed  Google Scholar 

Breijyeh Z, Karaman R (2020) Comprehensive review on Alzheimer’s disease: causes and treatment. Molecules. https://doi.org/10.3390/molecules25245789

Article  PubMed  PubMed Central  Google Scholar 

Cagnin A, Pigato G, Pettenuzzo I, Zorzi G, Roiter B, Anglani MG, Bussé C, Mozzetta S, Gabelli C, Campi C, Cecchin D (2024) Data-driven analysis of regional brain metabolism in behavioral frontotemporal dementia and late-onset primary psychiatric diseases with frontal lobe syndrome: a pet/mri study. Neurobiol Aging. https://doi.org/10.1016/j.neurobiolaging.2024.01.015

Article  PubMed  Google Scholar 

Cai Y, Peng Z, He Q, Sun P (2024) Behavioral variant frontotemporal dementia associated with GRN and ErbB4 gene mutations: a case report and literature review. BMC Med Genom 17(1):43. https://doi.org/10.1186/s12920-024-01819-5

Article  Google Scholar 

Calub Gabriel Ivan A, Elefante Erickson N, Galisanao, Jose Colin A, Iguid Sofia Lyn Beatrice G, Salise Jeremae C, Prado Seigfred V (2023) EEG-based classification of stages of Alzheimer’s disease (AD) and mild cognitive impairment (MCI). In: 5th international conference on bio-engineering for smart technologies (BioSMART), pp 1–6. https://doi.org/10.1109/BioSMART58455.2023.10162117

Cejnek M, Vyšata O, Valis M, Bukovsky I (2021) Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG. Med Biol Eng Compu 59:1–10. https://doi.org/10.1007/s11517-021-02427-6

Article  Google Scholar 

Chang J, Chang C (2023) Quantitative electroencephalography markers for an accurate diagnosis of frontotemporal dementia: a spectral power ratio approach. Medicina 59(12):2155. https://doi.org/10.3390/medicina59122155

Article  PubMed  PubMed Central  Google Scholar 

Chedid N, Tabbal J, Kabbara A, Allouch S (2022) The development of an automated machine learning pipeline for the detection of Alzheimer’s disease. Sci Rep 12:18137. https://doi.org/10.1038/s41598-022-22979-3

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen Y, Wang H, Zhang D, Zhang L, Tao L (2023) Multi-feature fusion learning for Alzheimer’s disease prediction using EEG signals in resting state. Front Neurosci 17

Dauwels J, Vialatte F, Musha T, Cichocki A (2010) A comparative study of synchrony measures for the early diagnosis of Alzheimer’s disease based on EEG. Neuroimage 49(1):668–693. https://doi.org/10.1016/j.neuroimage.2009.06.056

Article  CAS  PubMed  Google Scholar 

Ding Y, Chu Y, Liu M, Ling Z, Wang S, Li X, Li Y (2022) Fully automated discrimination of Alzheimer’s disease using resting-state Electroencephalography signals. Quant Imaging Med Surg 12:1063. https://doi.org/10.21037/qims-21-430

Article  PubMed  PubMed Central  Google Scholar 

Fouad IA, Labib FE-ZM (2023) Identification of Alzheimer’s disease from central lobe EEG signals utilizing machine learning and residual neural network. Biomed Signal Process Control 86:105266. https://doi.org/10.1016/j.bspc.2023.105266

Article  Google Scholar 

Geng D, Wang C, Fu Z, Zhang Y, Yang K, An H (2022) Sleep EEG-based approach to detect mild cognitive impairment. Front Aging Neurosci. https://doi.org/10.3389/fnagi.2022.865558

Article  PubMed  PubMed Central  Google Scholar 

Ghorbanian P, Devilbiss D, Hess T, Bernstein A, Simon A, Ashrafiuon H (2015) Exploration of EEG features of Alzheimer’s disease using continuous wavelet transform. Med Biol Eng Comput. https://doi.org/10.1007/s11517-015-1298-3

Article  PubMed  Google Scholar 

Ho TKK, Jeon Y, Na E, Ullah Z, Kim BC, Lee KH, Song J-I, Gwak J (2021) DeepADNet: a CNN-LSTM model for the multi-class classification of Alzheimer’s disease using multichannel EEG. Alzheimer’s Dement 17:057573. https://doi.org/10.1002/alz.057573

Article  Google Scholar 

Jiao B, Li R, Zhou H, Qing K, Liu H, Pan H, Lei Y, Fu W, Wang X, Xiao X (2023) Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology. Alzheimer’s Res Ther 15(1):1–14. https://doi.org/10.1186/s13195-023-01181-1

Article  Google Scholar 

Kachare P, Puri D, Sangle SB, Al-Shourbaji I, Jabbari A, Kirner R, Alameen A, Migdady H, Abualigah L (2024) LCADNet: a novel light CNN architecture for EEG-based Alzheimer disease detection. Phys Eng Sci Med. https://doi.org/10.1007/s13246-024-01425-w

Article  PubMed  Google Scholar 

Kılıç B, Aydın S (2022) Classification of contrasting discrete emotional states indicated by EEG based graph theoretical network measures. Neuroinformatics 20(4):863–877. https://doi.org/10.1007/s12021-022-09579-2

Article  PubMed  Google Scholar 

Lo Giudice P, Mammone N, Morabito F, Pizzimenti R, Ursino D, Virgili L (2019) Leveraging network analysis to support experts in their analyses of subjects with MCI and AD. Med Biol Eng Comput. https://doi.org/10.1007/s11517-019-02004-y

Article  PubMed  Google Scholar 

Miltiadous A, Tzimourta KD, Giannakeas N, Tsipouras MG, Afrantou T, Ioannidis P, Tzallas AT (2021) Alzheimer’s disease and frontotemporal dementia: a robust classification method of EEG signals and a comparison of validation methods. Diagnostics. https://doi.org/10.3390/diagnostics11081437

Miltiadous A, Tzimourta KD, Afrantou T, Ioannidis P, Grigoriadis N, Tsalikakis DG, Angelidis P, Tsipouras MG, Glavas E, Giannakeas N, Tzallas AT (2023) A dataset of scalp EEG recordings of Alzheimer’s disease, frontotemporal dementia and healthy subjects from routine EEG. Data. https://doi.org/10.3390/data8060095

Article  Google Scholar 

Miltiadous A, Gionanidis E, Tzimourta KD, Giannakeas N, Tzallas AT (2023) Dice-net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals. IEEE Access 11:71840–71858. https://doi.org/10.1109/ACCESS.2023.3294618

Article  Google Scholar 

Modir A, Shamekhi S, Ghaderyan P (2023) A systematic review and methodological analysis of EEG-based biomarkers of Alzheimer’s disease. Measurement 220:113274. https://doi.org/10.1016/j.measurement.2023.113274

Article  Google Scholar 

Nour M, Senturk U, Polat K (2024) A novel hybrid model in the diagnosis and classification of Alzheimer’s disease using EEG signals: deep ensemble learning (DEL) approach. Biomed Signal Process Control 89:105751. https://doi.org/10.1016/j.bspc.2023.105751

Article  Google Scholar 

Radhika T, Chandrasekar A, Vijayakumar V, Zhu Q (2023) Analysis of Markovian jump stochastic Cohen-Grossberg bam neural networks with time delays for exponential input-to-state stability. Neural Process Lett 55(8):11055–11072. https://doi.org/10.1007/s11063-023-11364-4

Article  Google Scholar 

Ravikanti DK, Saravanan S (2023) EEGAlzheimer’sNet: development of transformer-based attention long short term memory network for detecting Alzheimer disease using EEG signal. Biomed Signal Process Control 86:105318. https://doi.org/10.1016/j.bspc.2023.105318

Article  Google Scholar 

Sadegh-Zadeh S-A, Fakhri E, Bahrami M, Bagheri E, Khamsehashari R, Noroozian M, Hajiyavand AM (2023) An approach toward artificial intelligence Alzheimer’s disease diagnosis using brain signals. Diagnostics 13(3):477

Article  PubMed  PubMed Central  Google Scholar 

Safi MS, Safi SMM (2021) Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters. Biomed Signal Process Control 65:102338. https://doi.org/10.1016/j.bspc.2020.102338

Article  Google Scholar 

Siuly S, Alcin OF, Wang H, Li Y, Wen P (2024) Exploring rhythms and channels-based EEG biomarkers for early detection of Alzheimer’s disease. IEEE Trans Emerg Top Comput Intell. https://doi.org/10.1109/TETCI.2024.3353610

Article  Google Scholar 

Tharwat A (2021) Classification assessment methods. Appl Comput Inform 17:168–192. https://doi.org/10.1016/j.aci.2018.08.003

Article  Google Scholar 

Wang W, Sun D (2021) The improved adaboost algorithms for imbalanced data classification. Inf Sci 563:358–374. https://doi.org/10.1016/j.ins.2021.03.042

Article  Google Scholar 

Wang Z, Song J, Wang Y, Liu W (2023) Alzheimer’s disease classification detection based on brain electrical signal graph structure. In: 2023 3rd international conference on frontiers of electronics, information and computation technologies (ICFEICT), pp 294–300. https://doi.org/10.1109/ICFEICT59519.2023.00057

Yuan S, Liu J-X, Shang J, Kong X, Ma Z (2018) The earth mover’s distance and Bayesian linear discriminant analysis for epileptic seizure detection in scalp EEG. Biomed Eng Lett. https://doi.org/10.1007/s13534-018-0082-3

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