Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG

Clinical diagnosis of Alzheimer's disease: Report of the NINCDS‐ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease

McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915 · see citing works

A theory for multiresolution signal decomposition: the wavelet representation

Mallat, S. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.192463 · Cited by 21,463 · see citing works

Numerical recipes in C: the art of scientific computing

(1993) · Choice Reviews Online · doi:10.5860/choice.30-5638 · Cited by 17,933 · see citing works

Numerical recipes in Pascal: the art of scientific computing

(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 12,040 · see citing works

Mild Cognitive Impairment: Clinical Characterization and Outcome

Petersen, R. C., Smith, G. E., Waring, S. C., Ivnik, R. J., Tangalos, E. G., & Kokmen, E. (1999) · Archives of Neurology · doi:10.1001/archneur.56.3.303 · Cited by 8,980 · see citing works

The wavelet transform, time-frequency localization and signal analysis

Daubechies, I. (1990) · IEEE Transactions on Information Theory · doi:10.1109/18.57199 · Cited by 6,483 · see citing works

Backpropagation through time: what it does and how to do it

Werbos, P. J. (1990) · Proceedings of the IEEE · doi:10.1109/5.58337 · Cited by 4,879 · see citing works

Consortium to Establish a Registry for Alzheimer's Disease (CERAD)

Fillenbaum, G., & Heyman, A. (2002) · doi:10.1002/0470846410.ch42(iii · Cited by 2,859 · see citing works

Wavelets and filter banks: theory and design

Vetterli, M., & Herley, C. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.157221 · Cited by 1,693 · see citing works

EEG analysis based on time domain properties

Hjorth, B. (1970) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(70)90143-4 · Cited by 1,504 · see citing works

The Clinical Diagnosis of Alzheimer's Disease

Wade, J. P. H., Mirsen, T. R., Hachinski, V. C., Fisman, M., Lau, C. S., & Merskey, H. (1987) · Archives of Neurology · doi:10.1001/archneur.1987.00520130016010 · Cited by 1,416 · see citing works

Progress in supervised neural networks

Hush, D. R., & Horne, B. G. (1993) · IEEE Signal Processing Magazine · doi:10.1109/79.180705 · Cited by 1,252 · see citing works

Digital Signal Processing: A Practical Approach

Ifeachor, E., Jervis, B. W., Dagless, E. L., & O'Reilly, J. O. (1993) · Medical Entomology and Zoology · Cited by 1,220 · see citing works

Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks

Saad, E., Prokhorov, D. V., & Wunsch, D. C. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/72.728395 · Cited by 537 · see citing works

Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks

Puskorius, G. V., & Feldkamp, L. A. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279191 · Cited by 531 · see citing works

The Consortium to Establish a Registry for Alzheimer's Disease (CERAD): Part IV. Rates of cognitive change in the longitudinal assessment of probable Alzheimer's disease

Morris, J. C., Edland, S., Clark, C., Galasko, D., Koss, E., Mohs, R., van Belle, G., Fillenbaum, G., & Heyman, A. (1993) · Neurology · doi:10.1212/wnl.43.12.2457 · Cited by 441 · see citing works

Recurrent neural network based prediction of epileptic seizures in intra- and extracranial EEG

Petrosian, A. A., Prokhorov, D. V., Homan, R. W., Dasheiff, R. M., & Wunsch, D. C. (2000) · Neurocomputing · doi:10.1016/s0925-2312(99)00126-5 · Cited by 269 · see citing works

Brain electrical activity in patients with presenile and senile dementia of the Alzheimer type

Duffy, F. H., Albert, M., & McAnulty, G. B. (1984) · Annals of Neurology · doi:10.1002/ana.410160404 · Cited by 249 · see citing works

EEG-based, neural-net predictive classification of Alzheimer's disease versus control subjects is augmented by non-linear EEG measures

Pritchard, W. S., Duke, D. W., Coburn, K. L., Moore, N. C., Tucker, K. A., Jann, M. W., & Hostetler, R. M. (1994) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(94)90033-7 · Cited by 163 · see citing works

Longitudinal EEG spectral analysis in early stage of Alzheimer's disease

Soininen, H. S., Partanen, J. V., Laulumaa, V., Helkala, E.-L., Laakso, M. P., & Riekkinen, P. J. (1989) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(89)90064-3 · Cited by 144 · see citing works

Incorrect Diagnosis of Alzheimer's Disease: A Clinicopathologic Study

Klatka, L. A. (1996) · Archives of Neurology · doi:10.1001/archneur.1996.00550010045015 · Cited by 136 · see citing works

Diagnostic efficacy of computerized spectral versus visual EEG analysis in elderly normal, demented and depressed subjects

Brenner, R. P., Reynolds, C. F., & Ulrich, R. F. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90206-4 · Cited by 133 · see citing works

A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification

Feldkamp, L. A., & Puskorius, G. V. (1998) · Proceedings of the IEEE · doi:10.1109/5.726790 · Cited by 123 · see citing works

Replication of a study of frequency analysis of the resting awake EEG in mild probabke Alzheimer's disease

Coben, L. A., Chi, D., Snyder, A. Z., & Storandt, M. (1990) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(90)90168-j · Cited by 115 · see citing works

EEG spectral analysis in vascular and Alzheimer dementia

Signorino, M., Pucci, E., Belardinelli, N., Nolfe, G., & Angeleri, F. (1995) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(94)00290-2 · Cited by 111 · see citing works

Non-linear dynamical analysis of multichannel EEG: Clinical applications in dementia and Parkinson's disease

Stam, K. J., Tavy, D. L. J., Jelles, B., Achtereekte, H. A. M., Slaets, J. P. J., & Keunen, R. W. M. (1994) · Brain Topography · doi:10.1007/bf01186772 · Cited by 95 · see citing works

Quantitative EEG mapping and PET in Alzheimer's disease

Szelies, B., Grond, M., Herholz, K., Kessler, J., Wullen, T., & Heiss, W. (1992) · Journal of the Neurological Sciences · doi:10.1016/0022-510x(92)90008-9 · Cited by 88 · see citing works

Slowing on Quantitative Spectral EEG Is a Marker for Rate of Subsequent Cognitive and Functional Decline in Early Alzheimer Disease

Claus, J. J., Kwa, V. I. H., Teunisse, S., Walstra, G. J. M., van Gool, W. A., Koelman, J. H. T. M., Bour, L. J., & de Visser, B. W. O. (1998) · Alzheimer Disease & Associated Disorders · doi:10.1097/00002093-199809000-00008 · Cited by 80 · see citing works

Chaotic responses of the hippocampal CA3 region to a mossy fiber stimulation in vitro

Hayashi, H., & Ishizuka, S. (1995) · Brain Research · doi:10.1016/0006-8993(95)00485-9 · Cited by 76 · see citing works

Quantitative EEG in Frontotemporal Dementia

Yener, G. G., Leuchter, A. F., Jenden, D., Read, S. L., Cummings, J. L., & Miller, B. L. (1996) · Clinical Electroencephalography · doi:10.1177/155005949602700204 · Cited by 71 · see citing works

The pharmacological effects of ginkgo biloba, a plant extract, on the brain of dementia patients in comparison with tacrine.

Itil, T. M., Eralp, E. E., Ahmed, I., Kunitz, A., & Z, K. (1998) · PubMed · Cited by 69 · see citing works

Altered EEG Dynamical Responsivity Associated with Normal Aging and Probable Alzheimer's Disease

Pritchard, W. S., Duke, D. W., & Coburn, K. L. (1991) · Dementia and Geriatric Cognitive Disorders · doi:10.1159/000107183 · Cited by 66 · see citing works

Cognitive decline unlike normal aging is associated with alterations of EEG temporo-spatial characteristics

Stevens, A., & Kircher, T. T. J. (1998) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s004060050047 · Cited by 64 · see citing works

Diagnostic Usefulness of Linear and Nonlinear Quantitative EEG Analysis in Alzheimer's Disease

Stam, C. J., Jelles, B., Achtereekte, H. A. M., van Birgelen, J. H., & Slaets, J. P. J. (1996) · Clinical Electroencephalography · doi:10.1177/155005949602700205 · Cited by 58 · see citing works

Training controllers for robustness: multi-stream DEKF

Feldkamp, L. A., & Puskorius, G. V. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1994.374591 · Cited by 51 · see citing works

Reliability, validity and follow-up of the EEG in senile dementia: sequelae of sequential measurement

Hooijer, C., Jonker, C., Posthuma, J., & Visser, S. L. (1990) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(90)90094-z · Cited by 46 · see citing works

Topography of the maximum of the amplitude of EEG frequency bands in dementia of the Alzheimer type

Ihl, R., Dierks, T., Martin, E.-M., Froelich, L., & Maurer, K. (1996) · Biological Psychiatry · doi:10.1016/0006-3223(95)00174-3 · Cited by 42 · see citing works

Electroencephalographic abnormalities in patients with presenile dementia of the Alzheimer type: Quantitative analysis at rest and during photic stimulation

Wada, Y., Nanbu, Y., Jiang, Z.-Y., Koshino, Y., Yamaguchi, N., & Hashimoto, T. (1997) · Biological Psychiatry · doi:10.1016/0006-3223(95)00651-6 · Cited by 38 · see citing works

Topography of mobility and complexity parameters of the EEG in Alzheimer's Disease

Martín‐Loeches, M., García–Trapero, J., Gil, P., & Rubia, F. J. (1991) · Biological Psychiatry · doi:10.1016/0006-3223(91)90181-k · Cited by 17 · see citing works

Two-Hz wide EEG bands in Alzheimer's disease

Martín‐Loeches, M., Gil, P., & Rubia, F. J. (1993) · Biological Psychiatry · doi:10.1016/0006-3223(93)90134-y · Cited by 11 · see citing works

Classification of epileptic EEG using neural network and wavelet transform

Petrosian, A. A., Homan, R. W., Prokhorov, D. V., & Wunsch, D. C. (1996) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.255307 · Cited by 9 · see citing works

EEGs in Cognitive Stages of Alzheimerʼs-Type Dementia

Grusendorf, P. E. (1994) · Journal of Neuroscience Nursing · doi:10.1097/01376517-199402000-00007 · Cited by 7 · see citing works

The quantitative EEG in early and differential diagnosis of mild dementia of different genesis

Изнак, А. Ф., Колыхалов, И. В., Zhygulskaya, S. E., Vasilieva, A. G., Selezneva, A. G., & Селезнева, Н. Д. (1998) · European Neuropsychopharmacology · doi:10.1016/s0924-977x(98)80524-5 · Cited by 2 · see citing works

[Quantitative EEG in early and differential diagnosis of mild dementia of different genesis].

Изнак, А. Ф., Si, G., Zhigulskaya, S. E., Kolykhanov, I. V., Nv, C., Vasil'eva, A. G., Селезнева, Н. Д., & Zharikov, G. A. (1999) · PubMed · Cited by 1 · see citing works

EEG dynamics in patients with Alzheimer's disease

Jeong, J. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.01.001 · Cited by 1,346 · see citing works

Recurrent neural networks employing Lyapunov exponents for EEG signals classification

Güler, İ., Übeylı, E. D., & Güler, İ. (2005) · Expert Systems with Applications · doi:10.1016/j.eswa.2005.04.011 · Cited by 503 · see citing works

Machine Learning Techniques for the Diagnosis of Alzheimer’s Disease

Tanveer, M., Richhariya, B., Khan, R. U., Rashid, A. H., Khanna, P., Prasad, M., & Lin, C. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3344998 · Cited by 334 · see citing works

Innovative diagnostic tools for early detection of Alzheimer's disease

Laske, C., Sohrabi, H. R., Frost, S. M., López-de-Ipiña, K., Garrard, P., Buscema, M., Dauwels, J., Soekadar, S. R., Mueller, S., Linnemann, C., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2014.06.004 · Cited by 316 · see citing works

Automatic sleep stage recurrent neural classifier using energy features of EEG signals

Hsu, Y., Yang, Y., Wang, J.-S., & Hsu, C. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.11.003 · Cited by 298 · see citing works

Role of EEG as Biomarker in the Early Detection and Classification of Dementia

Al-Qazzaz, N. K., Ali, S. H. M., Ahmad, S. A., Chellappan, K., Islam, M. S., & Escudero, J. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/906038 · Cited by 222 · see citing works

Learning representations for the early detection of sepsis with deep neural networks

Kam, H. J., & Kim, H. Y. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.015 · Cited by 218 · see citing works

Medical Big Data: Neurological Diseases Diagnosis Through Medical Data Analysis

Siuly, S., & Zhang, Y. (2016) · Data Science and Engineering · doi:10.1007/s41019-016-0011-3 · Cited by 205 · see citing works

A spatio-temporal wavelet-chaos methodology for EEG-based diagnosis of Alzheimer's disease

Adeli, H., Ghosh‐Dastidar, S., & Dadmehr, N. (2008) · Neuroscience Letters · doi:10.1016/j.neulet.2008.08.008 · Cited by 194 · see citing works

PyEEG: An Open Source Python Module for EEG/MEG Feature Extraction

Bao, F. S., Liu, X., & Zhang, C. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/406391 · Cited by 180 · see citing works

Combining EEG signal processing with supervised methods for Alzheimer’s patients classification

Fiscon, G., Weitschek, E., Cialini, A., Felici, G., Bertolazzi, P., De Salvo, S., Bramanti, A., Bramanti, P., & De Cola, M. C. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0613-y · Cited by 172 · see citing works

Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods

Miltiadous, A., Tzimourta, K. D., Giannakeas, N., Tsipouras, M. G., Afrantou, T., Ioannidis, P., & Tzallas, A. T. (2021) · Diagnostics · doi:10.3390/diagnostics11081437 · Cited by 160 · see citing works

Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer’s Disease and Mild Cognitive Impairment

Ruiz-Gómez, S. J., Gómez, C., Poza, J., Gutiérrez‐Tobal, G. C., Tola-Arribas, M. Á., Cano, M., & Hornero, R. (2018) · Entropy · doi:10.3390/e20010035 · Cited by 156 · see citing works

Combining recurrent neural networks with eigenvector methods for classification of ECG beats

Übeylı, E. D. (2008) · Digital Signal Processing · doi:10.1016/j.dsp.2008.09.002 · Cited by 147 · see citing works

Alzheimer's Disease: Models of Computation and Analysis of EEGs

Adeli, H., Ghosh‐Dastidar, S., & Dadmehr, N. (2005) · Clinical EEG and Neuroscience · doi:10.1177/155005940503600303 · Cited by 141 · see citing works

Alzheimer's disease and models of computation: Imaging, classification, and neural models

Adeli, H., Ghosh‐Dastidar, S., & Dadmehr, N. (2005) · Journal of Alzheimer s Disease · doi:10.3233/jad-2005-7301 · Cited by 126 · see citing works

Recurrent neural networks employing Lyapunov exponents for analysis of ECG signals

Übeylı, E. D. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.06.022 · Cited by 124 · see citing works

Evidence-Based Evaluation of Diagnostic Accuracy of Resting EEG in Dementia and Mild Cognitive Impairment

Jelić, V., & Kowalski, J. H. (2009) · Clinical EEG and Neuroscience · doi:10.1177/155005940904000211 · Cited by 107 · see citing works

Automated EEG-Based Diagnosis of Neurological Disorders: Inventing the Future of Neurology

Adeli, H., & Ghosh‐Dastidar, S. (2010) · Thammasat University Digital Collections · Cited by 103 · see citing works

Is it possible to automatically distinguish resting EEG data of normal elderly vs. mild cognitive impairment subjects with high degree of accuracy?

Rossini, P. M., Buscema, M., Capriotti, M., Grossi, E., Rodriguez, G., Percio, C. D., & Babiloni, C. (2008) · Clinical Neurophysiology · doi:10.1016/j.clinph.2008.03.026 · Cited by 91 · see citing works

An automatic analysis method for detecting and eliminating ECG artifacts in EEG

Jiang, J., Chao, C.-F., Chiu, M., Lee, R.-G., Tseng, C.-L., & Lin, R. (2007) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2007.03.007 · Cited by 90 · see citing works

EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network

Alessandrini, M., Biagetti, G., Crippa, P., Falaschetti, L., Luzzi, S., & Turchetti, C. (2022) · Sensors · doi:10.3390/s22103696 · Cited by 89 · see citing works

Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks

Übeylı, E. D. (2008) · Digital Signal Processing · doi:10.1016/j.dsp.2008.07.007 · Cited by 87 · see citing works

Distributed machine learning cloud teleophthalmology IoT for predicting AMD disease progression

Das, A., Rad, P., Choo, K. R., Nouhi, B., Lish, J., & Martel, J. R. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.10.050 · Cited by 85 · see citing works

Multiclass support vector machines for diagnosis of erythemato-squamous diseases

Übeylı, E. D. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.08.067 · Cited by 80 · see citing works

Multimodal EEG, MRI and PET data fusion for Alzheimer's disease diagnosis

Robi Polikar, A., Tilley, C., Hillis, B., & Clark, C. M. (2010) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2010.5627621 · Cited by 79 · see citing works

Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease

Robi Polikar, A., Topalis, A., Green, D., Kounios, J., & Clark, C. M. (2006) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2006.08.012 · Cited by 78 · see citing works

Exploring the Intrinsic Features of EEG Signals via Empirical Mode Decomposition for Depression Recognition

Shen, J. G., Zhang, Y., Liang, H., Zhao, Z., Dong, Q., Qian, K., Zhang, X., & Hu, B. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3221962 · Cited by 78 · see citing works

The IFAST model, a novel parallel nonlinear EEG analysis technique, distinguishes mild cognitive impairment and Alzheimer's disease patients with high degree of accuracy

Buscema, M., Rossini, P. M., Babiloni, C., & Grossi, E. (2007) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2007.02.006 · Cited by 77 · see citing works

An ensemble based data fusion approach for early diagnosis of Alzheimer’s disease

Robi Polikar, A., Topalis, A., Parikh, D., Green, D., Frymiare, J. L., Kounios, J., & Clark, C. M. (2006) · Information Fusion · doi:10.1016/j.inffus.2006.09.003 · Cited by 76 · see citing works

Analysis of MEG Background Activity in Alzheimer’s Disease Using Nonlinear Methods and ANFIS

Gómez, C., Hornero, R., Abásolo, D., Fernández, A., & Escudero, J. (2009) · Annals of Biomedical Engineering · doi:10.1007/s10439-008-9633-6 · Cited by 73 · see citing works

Cognitive MMN and P300 in mild cognitive impairment and Alzheimer’s disease: a high density EEG-3D vector field tomography approach

Papadaniil, C. D., Kosmidou, V. E., Tsolaki, A., Tsolaki, M., Kompatsiaris, I. (., & Hadjileontiadis, L. J. (2016) · Brain Research · doi:10.1016/j.brainres.2016.07.043 · Cited by 71 · see citing works

Lacsogram: A New EEG Tool to Diagnose Alzheimer's Disease

Rodrigues, P. M., Bispo, B. C., Garrett, C., Alves, D., Teixeira, J. P., & Freitas, D. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3069789 · Cited by 69 · see citing works

Fully automated discrimination of Alzheimer’s disease using resting-state electroencephalography signals

Ding, Y., Chu, Y., Liu, M., Ling, Z., Wang, S., Li, X., & Li, Y. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-430 · Cited by 65 · see citing works

Electroencephalogram processing using neural networks

Robert, C., Gaudy, J., & Limoge, A. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00033-0 · Cited by 67 · see citing works

Confused or not Confused?

Ni, Z., Yuksel, A. C., Ni, X., Mandel, M., & Xie, L. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107513 · Cited by 61 · see citing works

Machine learning approaches to the application of disease modifying therapy for sickle cell using classification models

Khalaf, M., Hussain, A. J., Keight, R., Al‐Jumeily, D., Fergus, P., Keenan, R., & Tso, P. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.10.043 · Cited by 53 · see citing works

Robust EEG Based Biomarkers to Detect Alzheimer’s Disease

Al-Nuaimi, A. H. H., Blūma, M., Al-Juboori, S., Eke, C. S., Jammeh, E., Sun, L., & Ifeachor, E. (2021) · Brain Sciences · doi:10.3390/brainsci11081026 · Cited by 49 · see citing works

Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference

Xu, P., Xiong, X., Xue, Q., Tian, Y., Peng, Y., Zhang, R., Li, P. Y., Wang, Y., & Yao, D. (2014) · Physiological Measurement · doi:10.1088/0967-3334/35/7/1279 · Cited by 49 · see citing works

Exploring Rhythms and Channels-Based EEG Biomarkers for Early Detection of Alzheimer's Disease

Siuly, S., Alçin, Ö. F., Wang, H., Li, Y., & Wen, P. P. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3353610 · Cited by 46 · see citing works

Human scalp EEG processing: Various soft computing approaches

Majumdar, K. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2011.07.004 · Cited by 46 · see citing works

EEGAlzheimer’sNet: Development of transformer-based attention long short term memory network for detecting Alzheimer disease using EEG signal

Ravikanti, D. K., & Saravanan, S. K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105318 · Cited by 40 · see citing works

Classification of snoring sound based on a recurrent neural network

Lim, S. J., Jang, S. J., Lim, J. Y., & Ko, J. H. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.020 · Cited by 37 · see citing works

A recurrent neural network classifier for Doppler ultrasound blood flow signals

Güler, İ., & Übeylı, E. D. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2006.03.001 · Cited by 37 · see citing works

An artificial intelligence approach to classify and analyse EEG traces

Castellaro, C., Favaro, G., Castellaro, A., Casagrande, A., Castellaro, S., Puthenparampil, D. V., & Salimbeni, C. F. (2002) · Neurophysiologie Clinique · doi:10.1016/s0987-7053(02)00302-7 · Cited by 35 · see citing works

Diagnosis of Alzheimer’s Disease in Developed and Developing Countries: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

Chávez‐Fumagalli, M. Á., Shrivastava, P., Aguilar-Pineda, J. A., Nieto-Montesinos, R., Dávila-Del-Carpio, G., Peralta-Mestas, A., Zeballos, C. C., Valdez-Lazo, G., Fernandez-Macedo, V., Pino‐Figueroa, A. J., et al. (2020) · Journal of Alzheimer s Disease Reports · doi:10.3233/adr-200263 · Cited by 34 · see citing works

Comparison between KNN and ANN Classification in Brain Balancing Application via Spectrogram Image

Mustafa, M., Taib, M. N., Murat, Z. H., & Sulaiman, N. (2012) · Journal of Computer Science & Computational Mathematics · doi:10.20967/jcscm.2012.04.004 · Cited by 32 · see citing works

Clinician’s Road Map to Wavelet EEG as an Alzheimer’s disease Biomarker

Kanda, P. A. M., Trambaiolli, L. R., Lorena, A. C., Fraga, F. J., Basile, L., Nitríni, R., & Anghinah, R. (2013) · Clinical EEG and Neuroscience · doi:10.1177/1550059413486272 · Cited by 31 · see citing works

Electroencephalogram Signals for Detecting Confused Students in Online Education Platforms with Probability-Based Features

Daghriri, T., Rustam, F., Aljedaani, W., Bashiri, A. H., & Ashraf, I. (2022) · Electronics · doi:10.3390/electronics11182855 · Cited by 29 · see citing works

Recurrent Neural Networks in Medical Data Analysis and Classifications

Alaskar, H., Radi, N., & MacDermott, Á. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-803468-2.00007-2 · Cited by 27 · see citing works

Recurrent Neural Network Architectures for Analysing Biomedical Data Sets

Khalaf, M., Hussain, A. J., Keight, R., Al‐Jumeily, D., Keenan, R., Chalmers, C., Fergus, P., Salih, W. M., Abd, D. H., & Idowu, I. O. (2017) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese.2017.12 · Cited by 25 · see citing works

EEG Detection of Early Alzheimer's Disease Using Psychophysical Tasks

Sneddon, R., Shankle, W. R., Hara, J., Rodriquez, A., Hoffman, D. D., & Saha, U. (2005) · Clinical EEG and Neuroscience · doi:10.1177/155005940503600304 · Cited by 25 · see citing works

An Empirical Analysis of Different Machine Learning Techniques for Classification of EEG Signal to Detect Epileptic Seizure

Satapathy, S. K., Jagadev, A. K., & Dehuri, S. (2017) · Informatica · Cited by 23 · see citing works

A Dynamic Neural Network Architecture with Immunology Inspired Optimization for Weather Data Forecasting

Hussain, A. J., Liatsis, P., Khalaf, M., Tawfik, H., & Al-Asker, H. (2018) · Big Data Research · doi:10.1016/j.bdr.2018.04.002 · Cited by 23 · see citing works

Assessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease

Süt, N., & Senocak, M. S. (2007) · Expert Systems · doi:10.1111/j.1468-0394.2007.00425.x · Cited by 22 · see citing works

The Implicit Function as Squashing Time Model: A Novel Parallel Nonlinear EEG Analysis Technique Distinguishing Mild Cognitive Impairment and Alzheimer's Disease Subjects with High Degree of Accuracy

Buscema, M., Capriotti, M., Bergami, F., Babiloni, C., Rossini, P. M., & Grossi, E. (2007) · Computational Intelligence and Neuroscience · doi:10.1155/2007/35021 · Cited by 22 · see citing works

Deep learning adapted to differential neural networks used as pattern classification of electrophysiological signals

Llorente‐Vidrio, D., Ballesteros, M., Salgado, I., & Oria, J. I. C. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3066996 · Cited by 21 · see citing works

Individualized diagnosis of preclinical Alzheimer's Disease using deep neural networks

Park, J., Jang, S., Gwak, J., Kim, B. C., Lee, J., Choi, K. Y., Lee, K.-H., Sung, C.-J., Jang, G.-J., & Ahn, S.-K. (2022) · Expert systems with applications · doi:10.1016/j.eswa.2022.118511 · Cited by 16 · see citing works

Monitoring anesthesia using neural networks: A survey

Robert, C., Karasinski, P., Arreto, C.-D., & Gaudy, J. F. (2002) · Journal of Clinical Monitoring and Computing · doi:10.1023/a:1020783324797 · Cited by 21 · see citing works

Massively Parallel Classification of Single-Trial EEG Signals Using a Min-Max Modular Neural Network

Lu, B., Shin, J., & Ichikawa, M. (2004) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.821023 · Cited by 21 · see citing works

Early Diagnosis of Neurodegenerative Diseases Using CNN-LSTM and Wavelet Transform

Amooei, E., Sharifi, A., & Manthouri, M. (2023) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-023-00130-9 · Cited by 19 · see citing works

Elman neural network for the early identification of cognitive impairment in Alzheimer�s disease

Bertè, F. (2014) · Functional Neurology · doi:10.11138/fneur/2014.29.1.057 · Cited by 19 · see citing works

Introduction

Satapathy, S. K., Dehuri, S., Jagadev, A. K., & Mishra, D. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817426-5.00001-6 · Cited by 18 · see citing works

Alzheimer’s Disease Recognition with Artificial Neural Networks

Rodrigues, P. M., & Teixeira, J. P. (2013) · IGI Global eBooks · doi:10.4018/978-1-4666-3667-5.ch007 · Cited by 16 · see citing works

EEG-based mild depression recognition using multi-kernel convolutional and spatial-temporal Feature

Fan, Y., Yu, R., Li, J., Zhu, J., & Li, X. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313499 · Cited by 16 · see citing works

An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry

Güney, G., Yigin, B. Ö., Guven, N., Alıcı, Y. H., Çolak, B., Erzın, G., & Saygılı, G. (2021) · Clinical Psychopharmacology and Neuroscience · doi:10.9758/cpn.2021.19.2.206 · Cited by 15 · see citing works

Recurrent neural networks employing Lyapunov exponents for analysis of doppler ultrasound signals

Übeylı, E. D. (2007) · Expert Systems with Applications · doi:10.1016/j.eswa.2007.04.002 · Cited by 15 · see citing works

Deep Learning-based framework for Autism functional MRI Image Classification

Yang, X., Sarraf, S., & Zhang, N. (2018) · Journal of the Arkansas Academy of Science · doi:10.54119/jaas.2018.7214 · Cited by 15 · see citing works

Usage of eigenvector methods to improve reliable classifier for Doppler ultrasound signals

Übeylı, E. D. (2008) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2008.02.003 · Cited by 13 · see citing works

Using a Convolutional Neural Network and Convolutional Long Short-term Memory to Automatically Detect Aneurysms on 2D Digital Subtraction Angiography Images: Framework Development and Validation

Liao, J., Liu, L., Duan, H., Huang, Y., Zhou, L., Chen, L., & Wang, C. (2022) · JMIR Medical Informatics · doi:10.2196/28880 · Cited by 13 · see citing works

Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions

Fotiadis, D. I. (2015) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-4666-8828-5 · Cited by 12 · see citing works

Alzheimer’s disease as reflected in self-figure drawings of diagnosed patients

Lev‐Wiesel, R., & Segev, E. (2003) · The Arts in Psychotherapy · doi:10.1016/s0197-4556(03)00022-4 · Cited by 12 · see citing works

Case Studies for Applications of Elman Recurrent Neural Networks

Übeylı, E. D., & �beyli, M. (2008) · InTech eBooks · doi:10.5772/5550 · Cited by 12 · see citing works

Synchrony of two uncoupled neurons under half wave sine current stimulation

Peng, Y., Wang, J., & Jian, Z. (2008) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2008.03.008 · Cited by 12 · see citing works

Power frequency and wavelet characteristics in differentiating between normal and Alzheimer EEG

Yagneswaran, S., Baker, M., & Petrosian, A. A. (2003) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2002.1134380 · Cited by 12 · see citing works

LiteLSTM Architecture for Deep Recurrent Neural Networks

Elsayed, N., ElSayed, Z., & Maida, A. S. (2022) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas48785.2022.9937585 · Cited by 10 · see citing works

Empirical Study on the Performance of the Classifiers in EEG Classification

Satapathy, S. K., Dehuri, S., Jagadev, A. K., & Mishra, D. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817426-5.00003-x · Cited by 10 · see citing works

Recognition of Tones in YorÙbÁ Speech: Experiments With Artificial Neural Networks

Odejobi, O. A. (2007) · Studies in computational intelligence · doi:10.1007/978-3-540-75398-8_2 · Cited by 10 · see citing works

Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection

Rodrigues, P. M., Freitas, D., Teixeira, J. P., Alves, D., & Garrett, C. (2017) · International Journal of Reliable and Quality E-Healthcare · doi:10.4018/ijrqeh.2018010104 · Cited by 9 · see citing works

Ensemble Techniques with Weighted Combination Rules for Early Diagnosis of Alzheimer's Disease

Stepenosky, N., Kounios, J., Clark, C. M., & Robi Polikar, A. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.1716347 · Cited by 8 · see citing works

Stacked Generalization for Early Diagnosis of Alzheimer's Disease

Gandhi, H., Green, D., Kounios, J., Clark, C. M., & Robi Polikar, A. (2006) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2006.260644 · Cited by 7 · see citing works

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer’s disease and normal aging

Liao, K., Martin, L. E., Fakorede, S., Brooks, W. M., Burns, J. M., & Devos, H. (2024) · Clinical Neurophysiology · doi:10.1016/j.clinph.2024.11.013 · Cited by 6 · see citing works

Detection of Alzheimer’s Disease Electroencephalogram Temporal Events

Rodrigues, P. M., Freitas, D., & Teixeira, J. P. (2013) · International Journal of Reliable and Quality E-Healthcare · doi:10.4018/ijrqeh.2013070104 · Cited by 6 · see citing works

The Development of Intelligent Patient-Centric Systems for Health Care

Caronongan, A. P., Gorgui-Naguib, H., & Naguib, R. N. G. (2018) · Healthcare delivery in the information age · doi:10.1007/978-3-319-72287-0_22 · Cited by 6 · see citing works

Classification of Student's Confusion Level in E-Learning using Machine Learning

(2019) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.b1092.1292s19 · Cited by 5 · see citing works

EFD in Comparison with EWT for Synthetic and EEG Signal Decomposition and Classification of Alzheimer’s Disease and Mild Cognitive Impairment

Rabie, S. H. M., Ghofrani, S., Barghamadi, H., & Eslami, M. (2025) · Annals of Biomedical Engineering · doi:10.1007/s10439-025-03898-6 · Cited by 4 · see citing works

Signal‐to‐noise ratios for measuring saliency of features extracted by eigenvector methods from ophthalmic arterial Doppler signals

Übeylı, E. D. (2008) · Expert Systems · doi:10.1111/j.1468-0394.2008.00450.x · Cited by 4 · see citing works

Combining multichannel ERP data for early diagnosis of Alzheimer's Disease

Ahiskali, M., Robi Polikar, A., Kounios, J., Green, D., & Clark, C. M. (2009) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2009.5109348 · Cited by 4 · see citing works

Improving the performance of empirical mode decomposition via Tsallis entropy: Application to Alzheimer EEG analysis

Lazar, P., Jayapathy, R., Torrents‐Barrena, J., Linda, M. M., Mol, M. B., Mohanalin, J., & Puig, D. (2018) · Bio-Medical Materials and Engineering · doi:10.3233/bme-181008 · Cited by 4 · see citing works

The Utilisation of Dynamic Neural Networks for Medical Data Classifications- Survey with Case Study

Hussain, A. J., Fergus, P., Al‐Jumeily, D., Alaskar, H., & Radi, N. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22053-6_80 · Cited by 3 · see citing works

Proactive Alzheimer's Disease Identification Through EEG-Based Biomarkers

Bhendare, G., & Dewangan, L. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489551 · Cited by 3 · see citing works

LiteLSTM architecture based on weights sharing for recurrent neural networks

Elsayed, N., ElSayed, Z., & Maida, A. S. (2025) · International Journal of Computers and Applications · doi:10.1080/1206212x.2025.2499869 · Cited by 3 · see citing works

Study on Synchrony of Two Uncoupled Neurons under Slow Ramp Current Stimulation

Peng, Y., & Wang, J. (2009) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2009.298 · Cited by 3 · see citing works

Spatial regularization for neural network and application in Alzheimer's disease classification

Yang, X., Wu, Q., Hong, D., & Zou, J. (2016) · Future Technologies Conference · doi:10.1109/ftc.2016.7821700 · Cited by 3 · see citing works

Discrimination between Dementia Groups and Healthy Elderlies Using Scalp-Recorded-EEG-Based Brain Functional Connectivity Networks

Nishijima, S., Yada, T., Yamazaki, T., Kuroiwa, Y., Makoto, N., Fujino, K., Hirai, T., Baba, Y., Yamada, S., & Tsukiyama, S. (2020) · Journal of Biomedical Science and Engineering · doi:10.4236/jbise.2020.137015 · Cited by 3 · see citing works

Discharge Patterns' Change of Hindmarsh-Rose Neurons under Slow Ramp Current Stimulation

Peng, Y., & Wang, J. (2007) · International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA) · doi:10.1109/bicta.2007.4806417 · Cited by 3 · see citing works

Electroencephalogram Signal Analysis in Alzheimer's Disease Early Detection

Rodrigues, P. M., Freitas, D., Teixeira, J. P., Alves, D., & Garrett, C. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-3441-0.ch013 · Cited by 2 · see citing works

Evaluation Of Eeg Spectral Features In Alzheimer Disease Discrimination

Bispo, B., Carreres, A., Freitas, D., Rodrigues, P., & Teixeira, J. (2013) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · doi:10.5281/zenodo.43596 · Cited by 2 · see citing works

SPECTRAL ANALYSIS OF PREDICTIVE ERROR IN ALZHEIMER'S DISEASE DIAGNOSTICS

Tylová, L., Kukal, J., & Vyšata, O. (2013) · Neural Network World · doi:10.14311/nnw.2013.23.026 · Cited by 2 · see citing works

Neural Signal Processing

Hudson, D. L., & Cohen, M. E. (2007) · Springer eBooks · doi:10.1007/0-306-48610-5_6 · Cited by 2 · see citing works

The Authors

A, P. A., Prokhorov, D., V, Lajara-Nanson, W. A., B, S. R., Petrosian, A. A., & Ph., D. (2011)

Recurrent neural network and wavelet transform based distinction between Alzheimer and control EEG

Petrosian, A. A., Prokhorov, D. V., & Schiffer, R. B. (2003) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.1999.804351 · Cited by 4 · see citing works

Early recognition of Alzheimer's disease in EEG using recurrent neural network and wavelet transform

Petrosian, A. A., Prokhorov, D. V., & Schiffer, R. B. (2000) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.408570 · Cited by 5 · see citing works

Diagnosis of Alzheimer's disease and Mild Cognitive Impairment using EEG and Recurrent Neural Networks

Gkenios, G., Latsiou, K., Diamantaras, K., Chouvarda, I., & Tsolaki, M. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9871302 · Cited by 24 · see citing works

Dynamically stabilized recurrent neural network optimized with Artificial Gorilla Troops espoused Alzheimer’s disorder detection using EEG signals

Sudha, G., Saravanan, N., Muthalakshmi, M., & Birunda, M. (2024) · Health Information Science and Systems · doi:10.1007/s13755-024-00284-9 · Cited by 5 · see citing works