AI

Artificial intelligence in epilepsy — applications and pathways to the clinic


  • Kwan, P. & Brodie, M. J. Early identification of refractory epilepsy. N. Engl. J. Med. 342, 314–319 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • Lerner, J. T. et al. Assessment and surgical outcomes for mild type I and severe type II cortical dysplasia: a critical review and the UCLA experience. Epilepsia 50, 1310–1335 (2009).

    PubMed 

    Google Scholar
     

  • Hong, S. J. et al. Automated detection of cortical dysplasia type II in MRI-negative epilepsy. Neurology 83, 48–55 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ahmed, B. et al. Cortical feature analysis and machine learning improves detection of “MRI-negative” focal cortical dysplasia. Epilepsy Behav. 48, 21–28 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • El Azami, M. et al. Detection of lesions underlying intractable epilepsy on T1-weighted MRI as an outlier detection problem. PLoS ONE 11, e0161498 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alaverdyan, Z., Jung, J., Bouet, R. & Lartizien, C. Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: application to epilepsy lesion screening. Med. Image Anal. 60, 101618 (2020).

    PubMed 

    Google Scholar
     

  • Snyder, K. et al. Distinguishing type II focal cortical dysplasias from normal cortex: a novel normative modeling approach. NeuroImage Clin. 30, 102565 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, H. H. et al. Cognitive and epilepsy outcomes after epilepsy surgery caused by focal cortical dysplasia in children: early intervention maybe better. Childs Nerv. Syst. 30, 1885–1895 (2014).

    PubMed 

    Google Scholar
     

  • Adler, S. et al. Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy. NeuroImage Clin. 14, 18–27 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Spitzer, H. et al. Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study. Brain J. Neurol. 145, 3859–3871 (2022).


    Google Scholar
     

  • Wagstyl, K. et al. Atlas of lesion locations and postsurgical seizure freedom in focal cortical dysplasia: a MELD study. Epilepsia 63, 61–74 (2022).

    PubMed 

    Google Scholar
     

  • Jin, B. et al. Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning. Epilepsia 59, 982–992 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wagstyl, K. et al. Planning stereoelectroencephalography using automated lesion detection: retrospective feasibility study. Epilepsia 61, 1406–1416 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gill, R. S. et al. Multicenter validation of a deep learning detection algorithm for focal cortical dysplasia. Neurology 97, e1571–e1582 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chen, S. et al. Voxel-based morphometry analysis and machine learning based classification in pediatric mesial temporal lobe epilepsy with hippocampal sclerosis. Brain Imaging Behav. 14, 1945–1954 (2020).

    PubMed 

    Google Scholar
     

  • Park, Y. W. et al. Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls. Sci. Rep. 10, 19567 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mo, J. et al. Automated detection of hippocampal sclerosis using clinically empirical and radiomics features. Epilepsia 60, 2519–2529 (2019).

    PubMed 

    Google Scholar
     

  • Gleichgerrcht, E. et al. Radiological identification of temporal lobe epilepsy using artificial intelligence: a feasibility study. Brain Commun. 4, fcab284 (2022).

    PubMed 

    Google Scholar
     

  • Chang, A. J. et al. MRI-based deep learning can discriminate between temporal lobe epilepsy, Alzheimer’s disease, and healthy controls. Commun. Med. 3, 33 (2023).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hosseini, M. P., Nazem-Zadeh, M. R., Mahmoudi, F., Ying, H. & Soltanian-Zadeh, H. Support Vector Machine with nonlinear-kernel optimization for lateralization of epileptogenic hippocampus in MR images. Annu. Int. Conf. IEEE. Eng. Med. Biol. Soc. 2014, 1047–1050 (2014).

    PubMed 

    Google Scholar
     

  • Beheshti, I. et al. FLAIR-wise machine-learning classification and lateralization of MRI-negative 18F-FDG PET-positive temporal lobe epilepsy. Front. Neurol. 11, 580713 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Keihaninejad, S. et al. Classification and lateralization of temporal lobe epilepsies with and without hippocampal atrophy based on whole-brain automatic MRI segmentation. PLoS ONE 7, e33096 (2012).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bennett, O. F. et al. Learning to see the invisible: a data-driven approach to finding the underlying patterns of abnormality in visually normal brain magnetic resonance images in patients with temporal lobe epilepsy. Epilepsia 60, 2499–2507 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mahmoudi, F. et al. Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy. PLoS ONE 13, e0199137 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Caldairou, B. et al. MRI-based machine learning prediction framework to lateralize hippocampal sclerosis in patients with temporal lobe epilepsy. Neurology 97, e1583–e1593 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Louis, S. et al. Hippocampal sclerosis detection with NeuroQuant compared with neuroradiologists. AJNR Am. J. Neuroradiol. 41, 591–597 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hadar, P. N. et al. Clinical validation of automated hippocampal segmentation in temporal lobe epilepsy. NeuroImage Clin. 20, 1139–1147 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rebsamen, M. et al. A quantitative imaging biomarker supporting radiological assessment of hippocampal sclerosis derived from deep learning-based segmentation of T1w-MRI. Front. Neurol. 13, 812432 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pardoe, H. R. et al. High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI. Hum. Brain Mapp. 42, 2089–2098 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rudie, J. D., Colby, J. B. & Salamon, N. Machine learning classification of mesial temporal sclerosis in epilepsy patients. Epilepsy Res. 117, 63–69 (2015).

    PubMed 

    Google Scholar
     

  • Kim, D., Lee, J., Moon, J. & Moon, T. Interpretable deep learning-based hippocampal sclerosis classification. Epilepsia Open 7, 747–757 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Bernhardt, B. C., Hong, S. J., Bernasconi, A. & Bernasconi, N. Magnetic resonance imaging pattern learning in temporal lobe epilepsy: classification and prognostics. Ann. Neurol. 77, 436–446 (2015).

    PubMed 

    Google Scholar
     

  • Hong, S. J., Bernhardt, B., Schrader, D. S., Bernasconi, N. & Bernasconi, A. Whole-brain MRI phenotyping in dysplasia-related frontal lobe epilepsy. Neurology 86, 643–650 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mo, J. et al. Neuroimaging phenotyping and assessment of structural-metabolic-electrophysiological alterations in the temporal neocortex of focal cortical dysplasia IIIa. J. Magn. Reson. Imaging JMRI 54, 925–935 (2021).

    PubMed 

    Google Scholar
     

  • Lee, H. M. et al. Unsupervised machine learning reveals lesional variability in focal cortical dysplasia at mesoscopic scale. NeuroImage Clin. 28, 102438 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tustison, N. J. et al. The ANTsX ecosystem for quantitative biological and medical imaging. Sci. Rep. 11, 9068 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Henschel, L. et al. FastSurfer – a fast and accurate deep learning based neuroimaging pipeline. NeuroImage 219, 117012 (2020).

    PubMed 

    Google Scholar
     

  • Lucas, A. et al. iEEG‐recon: a fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices. Epilepsia 65, 817–829 (2024).

    PubMed 

    Google Scholar
     

  • Li, K. et al. Optimizing trajectories for cranial laser interstitial thermal therapy using computer-assisted planning: a machine learning approach. Neurotherapeutics 16, 182–191 (2019).

    CAS 
    PubMed 

    Google Scholar
     

  • Pérez-García, F. et al. Simulation of brain resection for cavity segmentation using self-supervised and semi-supervised learning. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (eds Martel, A. L. et al.) 115–125 (Springer, 2020).

  • Arnold, T. C. et al. Deep learning-based automated segmentation of resection cavities on postsurgical epilepsy MRI. NeuroImage Clin. 36, 103154 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pérez-García, F. et al. A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections. Int. J. Comput. Assist. Radiol. Surg. 16, 1653–1661 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sarikaya, I. PET studies in epilepsy. Am. J. Nucl. Med. Mol. Imaging 5, 416–430 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kerr, W. T. et al. Computer-aided diagnosis and localization of lateralized temporal lobe epilepsy using interictal FDG-PET. Front. Neurol. 4, 31 (2013).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Beheshti, I. et al. Pattern analysis of glucose metabolic brain data for lateralization of MRI-negative temporal lobe epilepsy. Epilepsy Res. 167, 106474 (2020).

    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, Q. et al. A deep learning framework for 18F-FDG PET imaging diagnosis in pediatric patients with temporal lobe epilepsy. Eur. J. Nucl. Med. Mol. Imaging 48, 2476–2485 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kini, L. G. et al. Quantitative [18]FDG PET asymmetry features predict long-term seizure recurrence in refractory epilepsy. Epilepsy Behav. 116, 107714 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sinclair, B. et al. Machine learning approaches for imaging-based prognostication of the outcome of surgery for mesial temporal lobe epilepsy. Epilepsia 63, 1081–1092 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Iglesias, J. E. et al. SynthSR: a public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry. Sci. Adv. 9, eadd3607 (2023).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lucas, A. et al. Multi-contrast high-field quality image synthesis for portable low-field MRI using generative adversarial networks and paired data. Preprint at medRxiv https://doi.org/10.1101/2023.12.28.23300409 (2023).

  • Flaus, A. et al. PET image enhancement using artificial intelligence for better characterization of epilepsy lesions. Front. Med. 9, 1042706 (2022).


    Google Scholar
     

  • Binder, J. R. FMRI is a valid noninvasive alternative to Wada testing. Epilepsy Behav. 20, 214–222 (2011).

    PubMed 

    Google Scholar
     

  • Janecek, J. K. et al. Language lateralization by fMRI and Wada testing in 229 epilepsy patients: rates and predictors of discordance. Epilepsia 54, 314–322 (2013).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gazit, T. et al. Probabilistic machine learning for the evaluation of presurgical language dominance. J. Neurosurg. 125, 481–493 (2016).

    CAS 
    PubMed 

    Google Scholar
     

  • Torlay, L., Perrone-Bertolotti, M., Thomas, E. & Baciu, M. Machine learning – XGBoost analysis of language networks to classify patients with epilepsy. Brain Inform. 4, 159–169 (2017).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kramer, M. A. & Cash, S. S. Epilepsy as a disorder of cortical network organization. Neuroscientist 18, 360–372 (2012).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pedersen, M., Curwood, E. K., Archer, J. S., Abbott, D. F. & Jackson, G. D. Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: multivariate analysis of task-free fMRI. Epilepsia 56, 1767–1773 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • Bharath, R. D. et al. Machine learning identifies “rsfMRI epilepsy networks” in temporal lobe epilepsy. Eur. Radiol. 29, 3496–3505 (2019).

    PubMed 

    Google Scholar
     

  • Dai, X. J., Liu, H., Yang, Y., Wang, Y. & Wan, F. Brain network excitatory/inhibitory imbalance is a biomarker for drug-naive Rolandic epilepsy: a radiomics strategy. Epilepsia 62, 2426–2438 (2021).

    CAS 
    PubMed 

    Google Scholar
     

  • Hwang, G. et al. Using low-frequency oscillations to detect temporal lobe epilepsy with machine learning. Brain Connect. 9, 184–193 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mazrooyisebdani, M. et al. Graph theory analysis of functional connectivity combined with machine learning approaches demonstrates widespread network differences and predicts clinical variables in temporal lobe epilepsy. Brain Connect. 10, 39–50 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gholipour, T. et al. Common functional connectivity alterations in focal epilepsies identified by machine learning. Epilepsia 63, 629–640 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hao, S., Yang, C., Li, Z. & Ren, J. Distinguishing patients with temporal lobe epilepsy from normal controls with the directed graph measures of resting-state fMRI. Seizure 96, 25–33 (2022).

    PubMed 

    Google Scholar
     

  • Nguyen, R. D. et al. Convolutional neural networks for pediatric refractory epilepsy classification using resting-state functional magnetic resonance imaging. World Neurosurg. 149, e1112–e1122 (2021).

    PubMed 

    Google Scholar
     

  • Chiang, S., Levin, H. S. & Haneef, Z. Computer-automated focus lateralization of temporal lobe epilepsy using fMRI. J. Magn. Reson. Imaging 41, 1689–1694 (2015).

    PubMed 

    Google Scholar
     

  • Yang, Z., Choupan, J., Reutens, D. & Hocking, J. Lateralization of temporal lobe epilepsy based on resting-state functional magnetic resonance imaging and machine learning. Front. Neurol. 6, 184 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fallahi, A. et al. Dynamic functional connectivity in temporal lobe epilepsy: a graph theoretical and machine learning approach. Neurol. Sci. 42, 2379–2390 (2021).

    PubMed 

    Google Scholar
     

  • Hunyadi, B. et al. A prospective fMRI-based technique for localising the epileptogenic zone in presurgical evaluation of epilepsy. NeuroImage 113, 329–339 (2015).

    PubMed 

    Google Scholar
     

  • Nandakumar, N., Hsu, D., Ahmed, R. & Venkataraman, A. DeepEZ: a graph convolutional network for automated epileptogenic zone localization from resting-state fMRI connectivity. IEEE Trans. Biomed. Eng. 70, 216–227 (2023).

    PubMed 

    Google Scholar
     

  • He, X. et al. Presurgical thalamic “hubness” predicts surgical outcome in temporal lobe epilepsy. Neurology 88, 2285–2293 (2017).

    PubMed 

    Google Scholar
     

  • Wang, X. et al. Graph-theory based degree centrality combined with machine learning algorithms can predict response to treatment with antiepileptic medications in children with epilepsy. J. Clin. Neurosci. 91, 276–282 (2021).

    CAS 
    PubMed 

    Google Scholar
     

  • Garner, R. et al. A machine learning model to predict seizure susceptibility from resting-state fMRI connectivity. In 2019 Spring Simulation Conference https://doi.org/10.23919/springsim.2019.8732859 (IEEE, 2019).

  • Amarreh, I., Meyerand, M. E., Stafstrom, C., Hermann, B. P. & Birn, R. M. Individual classification of children with epilepsy using support vector machine with multiple indices of diffusion tensor imaging. NeuroImage Clin. 4, 757–764 (2014).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Focke, N. K. et al. Automated MR image classification in temporal lobe epilepsy. NeuroImage 59, 356–362 (2012).

    PubMed 

    Google Scholar
     

  • Del Gaizo, J. et al. Using machine learning to classify temporal lobe epilepsy based on diffusion MRI. Brain Behav. 7, e00801 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Fang, P. et al. Mapping the convergent temporal epileptic network in left and right temporal lobe epilepsy. Neurosci. Lett. 639, 179–184 (2017).

    CAS 
    PubMed 

    Google Scholar
     

  • Huang, J., Xu, J., Kang, L. & Zhang, T. Identifying epilepsy based on deep learning using DKI images. Front. Hum. Neurosci. 14, 590815 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • An, J. et al. Decreased white matter integrity in mesial temporal lobe epilepsy: a machine learning approach. NeuroReport 25, 788 (2014).

    PubMed 

    Google Scholar
     

  • Kamiya, K. et al. Machine learning of DTI structural brain connectomes for lateralization of temporal lobe epilepsy. Magn. Reson. Med. Sci. 15, 121–129 (2016).

    PubMed 

    Google Scholar
     

  • Munsell, B. C. et al. Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. NeuroImage 118, 219–230 (2015).

    PubMed 

    Google Scholar
     

  • Taylor, P. N. et al. The impact of epilepsy surgery on the structural connectome and its relation to outcome. NeuroImage Clin. 18, 202–214 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sinha, N. et al. Structural brain network abnormalities and the probability of seizure recurrence after epilepsy surgery. Neurology 96, e758–e771 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gleichgerrcht, E. et al. Deep learning applied to whole-brain connectome to determine seizure control after epilepsy surgery. Epilepsia 59, 1643–1654 (2018).

    PubMed 

    Google Scholar
     

  • Gleichgerrcht, E. et al. Temporal lobe epilepsy surgical outcomes can be inferred based on structural connectome hubs: a machine learning study. Ann. Neurol. 88, 970–983 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Revell, A. Y. et al. A framework for brain atlases: lessons from seizure dynamics. NeuroImage 254, 118986 (2022).

    PubMed 

    Google Scholar
     

  • Munsell, B. C. et al. Relationship between neuronal network architecture and naming performance in temporal lobe epilepsy: a connectome based approach using machine learning. Brain Lang. 193, 45–57 (2019).

    CAS 
    PubMed 

    Google Scholar
     

  • Jeong, J. W., Lee, M. H., O’Hara, N., Juhász, C. & Asano, E. Prediction of baseline expressive and receptive language function in children with focal epilepsy using diffusion tractography-based deep learning network. Epilepsy Behav. 117, 107909 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Peter Binding, L. et al. The impact of temporal lobe epilepsy surgery on picture naming and its relationship to network metric change. NeuroImage Clin. 38, 103444 (2023).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lee, M.-H. et al. Novel deep learning network analysis of electrical stimulation mapping-driven diffusion MRI tractography to improve preoperative evaluation of pediatric epilepsy. IEEE Trans. Biomed. Eng. 67, 3151–3162 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cantor-Rivera, D., Khan, A. R., Goubran, M., Mirsattari, S. M. & Peters, T. M. Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative MR imaging. Comput. Med. Imaging Graph. 41, 14–28 (2015).

    PubMed 

    Google Scholar
     

  • Huang, J. et al. Coherent pattern in multi-layer brain networks: application to epilepsy identification. IEEE J. Biomed. Health Inform. 24, 2609–2620 (2020).

    PubMed 

    Google Scholar
     

  • Zhou, B. et al. Machine learning for detecting mesial temporal lobe epilepsy by structural and functional neuroimaging. Front. Med. 14, 630–641 (2020).

    PubMed 

    Google Scholar
     

  • Pustina, D. et al. Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: a multimodal study. NeuroImage Clin. 9, 20–31 (2015).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sisodiya, S. M. et al. The ENIGMA-Epilepsy working group: mapping disease from large data sets. Hum. Brain Mapp. 43, 113–128 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gleichgerrcht, E. et al. Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: a worldwide ENIGMA-Epilepsy study. NeuroImage Clin. 31, 102765 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Tang, Y. et al. Machine learning-derived multimodal neuroimaging of presurgical target area to predict individual’s seizure outcomes after epilepsy surgery. Front. Cell Dev. Biol. 9, 669795 (2021).

    PubMed 

    Google Scholar
     

  • Lee, H. M. et al. Decomposing MRI phenotypic heterogeneity in epilepsy: a step towards personalized classification. Brain J. Neurol. 145, 897–908 (2022).


    Google Scholar
     

  • Lucas, A. et al. Mapping hippocampal and thalamic atrophy in epilepsy: a 7-T magnetic resonance imaging study. Epilepsia 65, 1092–1106 (2024).

    PubMed 

    Google Scholar
     

  • Rasheed, K. et al. Machine learning for predicting epileptic seizures using EEG signals: a review. IEEE Rev. Biomed. Eng. 14, 139–155 (2021).

    PubMed 

    Google Scholar
     

  • Siddiqui, M. K., Morales-Menendez, R., Huang, X. & Hussain, N. A review of epileptic seizure detection using machine learning classifiers. Brain Inform. 7, 5 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Usman, S. M. et al. Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: review of available methodologies. Seizure 71, 258–269 (2019).

    PubMed 

    Google Scholar
     

  • Miltiadous, A. et al. Machine learning algorithms for epilepsy detection based on published EEG databases: a systematic review. IEEE Access. 11, 564–594 (2023).


    Google Scholar
     

  • Mercier, M. R. et al. Advances in human intracranial electroencephalography research, guidelines and good practices. NeuroImage 260, 119438 (2022).

    PubMed 

    Google Scholar
     

  • Litt, B. & Echauz, J. Prediction of epileptic seizures. Lancet Neurol. 1, 22–30 (2002).

    PubMed 

    Google Scholar
     

  • Andrzejak, R. G. et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E 64, 061907 (2001).

    CAS 

    Google Scholar
     

  • Andrzejak, R. G., Schindler, K. & Rummel, C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E 86, 046206 (2012).


    Google Scholar
     

  • Klatt, J. et al. The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia 53, 1669–1676 (2012).

    PubMed 

    Google Scholar
     

  • Brinkmann, B. H. et al. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139, 1713–1722 (2016).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kuhlmann, L. et al. Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG. Brain 141, 2619–2630 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baldassano, S. N. et al. Crowdsourcing seizure detection: algorithm development and validation on human implanted device recordings. Brain 140, 1680–1691 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wong, S. et al. EEG datasets for seizure detection and prediction – a review. Epilepsia Open. 8, 252–267 (2023).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mirowski P. W., LeCun, Y., Madhavan, D. & Kuzniecky, R. In 2008 IEEE Workshop on Machine Learning for Signal Processing (eds Principe, J. C, Erdogmus, D. & Adali, T) 244–249 (IEEE, 2008).

  • Park, Y., Luo, L., Parhi, K. K. & Netoff, T. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 52, 1761–1770 (2011).

    PubMed 

    Google Scholar
     

  • Wang, N. & Lyu, M. R. Extracting and selecting distinctive EEG features for efficient epileptic seizure prediction. IEEE J. Biomed. Health Inform. 19, 1648–1659 (2015).

    PubMed 

    Google Scholar
     

  • Richman, J. S. & Moorman, J. R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000).

    CAS 
    PubMed 

    Google Scholar
     

  • Song, Y. & Zhang, J. Discriminating preictal and interictal brain states in intracranial EEG by sample entropy and extreme learning machine. J. Neurosci. Methods 257, 45–54 (2016).

    PubMed 

    Google Scholar
     

  • Truong, N. D. et al. Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw. 105, 104–111 (2018).

    PubMed 

    Google Scholar
     

  • Kiral-Kornek, I. et al. Epileptic seizure prediction using big data and deep learning: toward a mobile system. EBioMedicine 27, 103–111 (2018).

    PubMed 

    Google Scholar
     

  • Chung, Y. G. et al. Deep convolutional neural network based interictal-preictal electroencephalography prediction: application to focal cortical dysplasia type-II. Front. Neurol. 11, 594679 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Eberlein, M. et al. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (eds Griol, D., Zheng, H. & Schmidt, H.) 2577–2582 (IEEE, 2018).

  • Yamamoto, S. et al. Data-driven electrophysiological feature based on deep learning to detect epileptic seizures. J. Neural Eng. 2021, 18, https://doi.org/10.1088/1741-2552/ac23bf (2021).

  • Sundararajan M, Taly A, Yan Q. Axiomatic attribution for deep networks. In International Conference on Machine Learning 3319–3328 (PMLR, 2017).

  • Bartolomei, F., Chauvel, P. & Wendling, F. Epileptogenicity of brain structures in human temporal lobe epilepsy: a quantified study from intracerebral EEG. Brain 131, 1818–1830 (2008).

    PubMed 

    Google Scholar
     

  • Wang, G. et al. Seizure prediction using directed transfer function and convolution neural network on intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. Publ. IEEE Eng. 28, 2711–2720 (2020).


    Google Scholar
     

  • Peng, P., Xie, L. & Wei, H. A deep Fourier neural network for seizure prediction using convolutional neural network and ratios of spectral power. Int. J. Neural Syst. 31, 2150022 (2021).

    PubMed 

    Google Scholar
     

  • Wu, X., Zhang, T., Zhang, L. & Qiao, L. Epileptic seizure prediction using successive variational mode decomposition and transformers deep learning network. Front. Neurosci. 16, 982541 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, Z. et al. Epileptic seizure prediction using deep neural networks via transfer learning and multi-feature fusion. Int. J. Neural Syst. 32, 2250032 (2022).

    PubMed 

    Google Scholar
     

  • Boonyakitanont, P., Lek-uthai, A., Chomtho, K. & Songsiri, J. A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed. Signal. Process. Control. 57, 101702 (2020).


    Google Scholar
     

  • Liu, G., Xiao, R., Xu, L. & Cai, J. Minireview of epilepsy detection techniques based on electroencephalogram signals. Front. Syst. Neurosci. 15, 685387 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Acharya, U. R., Vinitha Sree, S., Swapna, G., Martis, R. J. & Suri, J. S. Automated EEG analysis of epilepsy: a review. Knowl. Based Syst. 45, 147–165 (2013).


    Google Scholar
     

  • Ghosh-Dastidar, S. & Adeli, H. A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Netw. 22, 1419–1431 (2009).

    PubMed 

    Google Scholar
     

  • Faust, O., Acharya, U. R., Min, L. C. & Sputh, B. H. C. Automatic identification of epileptic and background EEG signals using frequency domain parameters. Int. J. Neural Syst. 20, 159–176 (2010).

    PubMed 

    Google Scholar
     

  • Kharbouch, A., Shoeb, A., Guttag, J. & Cash, S. S. An algorithm for seizure onset detection using intracranial EEG. Epilepsy Behav. 22, S29–S35 (2011).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, Y., Zhou, W., Yuan, Q. & Chen, S. Automatic seizure detection using wavelet transform and SVM in long-term intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. Publ. IEEE Eng. 20, 749–755 (2012).


    Google Scholar
     

  • Xiang, J. et al. The detection of epileptic seizure signals based on fuzzy entropy. J. Neurosci. Methods 243, 18–25 (2015).

    PubMed 

    Google Scholar
     

  • Zheng, Y. X., Zhu, J. M., Qi, Y., Zheng, X. X. & Zhang, J. M. An automatic patient-specific seizure onset detection method using intracranial electroencephalography. Neuromodulation 18, 79–84 (2015).

    PubMed 

    Google Scholar
     

  • Manzouri, F., Heller, S., Dümpelmann, M., Woias, P. & Schulze-Bonhage, A. A comparison of machine learning classifiers for energy-efficient implementation of seizure detection. Front. Syst. Neurosci. 12, 43 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ehrens, D., Cervenka, M. C., Bergey, G. K. & Jouny, C. C. Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset. Clin. Neurophysiol. 135, 85–95 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cook, M. J. et al. Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. Lancet Neurol. 12, 563–571 (2013).

    PubMed 

    Google Scholar
     

  • Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H. & Adeli, H. Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput. Biol. Med. 100, 270–278 (2018).

    PubMed 

    Google Scholar
     

  • Zhou, M. et al. Epileptic seizure detection based on EEG signals and CNN. Front. Neuroinform. 12, 95 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Gómez, C. et al. Automatic seizure detection based on imaged-EEG signals through fully convolutional networks. Sci. Rep. 10, 21833 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Li, Z. et al. Deep learning of simultaneous intracranial and scalp EEG for prediction, detection, and lateralization of mesial temporal lobe seizures. Front. Neurol. 12, 705119 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Caffarini, J. et al. Engineering nonlinear epileptic biomarkers using deep learning and Benford’s law. Sci. Rep. 12, 5397 (2022).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zanetti, R., Pale, U., Teijeiro, T. & Atienza, D. Approximate zero-crossing: a new interpretable, highly discriminative and low-complexity feature for EEG and iEEG seizure detection. J. Neural Eng. 18, 066018 (2022).


    Google Scholar
     

  • Revell, A. Y. et al. A taxonomy of seizure spread patterns, speed of spread, and associations with structural connectivity. Preprint at bioRxiv https://doi.org/10.1101/2022.10.24.513577 (2022).

  • Pattnaik, A. R. et al. The seizure severity score: a quantitative tool for comparing seizures and their response to therapy. J. Neural Eng. 20, https://doi.org/10.1088/1741-2552/aceca1 (2003).

  • Revell, A.Y. et al. White matter signals reflect information transmission between brain regions during seizures. Preprint at bioRxiv https://doi.org/10.1101/2021.09.15.460549 (2022).

  • Chen, D., Wan, S. & Bao, F. S. Epileptic focus localization using discrete wavelet transform based on interictal intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 413–425 (2017).

    PubMed 

    Google Scholar
     

  • Grinenko, O. et al. A fingerprint of the epileptogenic zone in human epilepsies. Brain J. Neurol. 141, 117–131 (2018).


    Google Scholar
     

  • Varatharajah, Y. et al. Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy. J. Neural Eng. 15, 046035 (2018).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cimbalnik, J. et al. Multi-feature localization of epileptic foci from interictal, intracranial EEG. Clin. Neurophysiol. 130, 1945–1953 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Klimes, P. et al. NREM sleep is the state of vigilance that best identifies the epileptogenic zone in the interictal electroencephalogram. Epilepsia 60, 2404–2415 (2019).

    PubMed 

    Google Scholar
     

  • Conrad, E. C. et al. Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy. Epilepsia 64, 754–768 (2023).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhao, X., Sole-Casals, J., Sugano, H. & Tanaka, T. Seizure onset zone classification based on imbalanced iEEG with data augmentation. J. Neural Eng. 19, 065001 (2022).


    Google Scholar
     

  • Rao, V. R. & Lowenstein, D. H. Epilepsy. Curr. Biol. 25, R742–R746 (2015).

    CAS 
    PubMed 

    Google Scholar
     

  • Antoniades, A. et al. Detection of interictal discharges with convolutional neural networks using discrete ordered multichannel intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 25, 2285–2294 (2017).

    PubMed 

    Google Scholar
     

  • Abou Jaoude, M. et al. Detection of mesial temporal lobe epileptiform discharges on intracranial electrodes using deep learning. Clin. Neurophysiol. 131, 133–141 (2020).

    PubMed 

    Google Scholar
     

  • Quon, R. J. et al. AiED: artificial intelligence for the detection of intracranial interictal epileptiform discharges. Clin. Neurophysiol. 133, 1–8 (2022).

    PubMed 

    Google Scholar
     

  • Zhang, Y. et al. Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach. Brain Commun. 4, fcab267 (2022).

    PubMed 

    Google Scholar
     

  • Zhang, Y. et al. Characterizing physiological high-frequency oscillations using deep learning. J. Neural. Eng. 19, 066027 (2022).


    Google Scholar
     

  • Medvedev, A. V., Agoureeva, G. I. & Murro, A. M. A long short-term memory neural network for the detection of epileptiform spikes and high frequency oscillations. Sci. Rep. 9, 19374 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Geng, D. et al. Deep learning for robust detection of interictal epileptiform discharges. J. Neural Eng. 18, 056015 (2021).


    Google Scholar
     

  • Baud, M. O. et al. Unsupervised learning of spatiotemporal interictal discharges in focal epilepsy. Neurosurgery 83, 683–691 (2018).

    PubMed 

    Google Scholar
     

  • Charupanit, K., Sen-Gupta, I., Lin, J. J. & Lopour, B. A. Detection of anomalous high-frequency events in human intracranial EEG. Epilepsia Open. 5, 263–273 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nejedly, P. et al. Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification. Sci. Rep. 13, 744 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jeong, J. W. et al. Multi-scale deep learning of clinically acquired multi-modal MRI improves the localization of seizure onset zone in children with drug-resistant epilepsy. IEEE J. Biomed. Health Inform. 26, 5529–5539 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mo, J. et al. Neuroimaging gradient alterations and epileptogenic prediction in focal cortical dysplasia IIIa. J. Neural Eng. 19, 025001 (2022).


    Google Scholar
     

  • Constantino, A. C. et al. Expert-level intracranial electroencephalogram ictal pattern detection by a deep learning neural network. Front. Neurol. 12, 603868 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Arcot Desai, S., Tcheng, T. & Morrell, M. Non-linear embedding methods for identifying similar brain activity in 1 million iEEG records captured from 256 RNS system patients. Front. Big Data 5, 840508 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stirling, R. E. et al. Seizure forecasting using a novel sub-scalp ultra-long term EEG monitoring system. Front. Neurol. 12, 713794 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ibrahim, G. M. et al. Presurgical thalamocortical connectivity is associated with response to vagus nerve stimulation in children with intractable epilepsy. NeuroImage Clin. 16, 634–642 (2017).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mithani, K. et al. Connectomic profiling identifies responders to vagus nerve stimulation. Ann. Neurol. 86, 743–753 (2019).

    PubMed 

    Google Scholar
     

  • Brinkmann, B. H. et al. Seizure diaries and forecasting with wearables: epilepsy monitoring outside the clinic. Front. Neurol. 12, 690404 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Meisel, C. et al. Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting. Epilepsia 61, 2653–2666 (2020).

    PubMed 

    Google Scholar
     

  • Nasseri, M. et al. Non-invasive wearable seizure detection using long-short-term memory networks with transfer learning. J. Neural Eng. 18, 056017 (2021).


    Google Scholar
     

  • Yew, A. N. J., Schraagen, M., Otte, W. M. & van Diessen, E. Transforming epilepsy research: a systematic review on natural language processing applications. Epilepsia 64, 292–305 (2023).

    PubMed 

    Google Scholar
     

  • Savova, G. K. et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17, 507–513 (2010).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cui, L., Bozorgi, A., Lhatoo, S. D., Zhang, G. Q. & Sahoo, S. S. EpiDEA: extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification. AMIA Annu. Symp. Proc. 2012, 1191–1200 (2012).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Guergana, K. S. et al. Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17, 507–513 (2010).


    Google Scholar
     

  • Garla, V. et al. The Yale cTAKES extensions for document classification: architecture and application. J. Am. Med. Inform. Assoc. 18, 614–620 (2011).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hamid, H. et al. Validating a natural language processing tool to exclude psychogenic nonepileptic seizures in electronic medical record-based epilepsy research. Epilepsy Behav. 29, 578–580 (2013).

    CAS 
    PubMed 

    Google Scholar
     

  • Beaulieu-Jones, B. K. et al. Predicting seizure recurrence after an initial seizure-like episode from routine clinical notes using large language models: a retrospective cohort study. Lancet Digit. Health 5, e882–e894 (2023).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xie, K. et al. Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing. J. Am. Med. Inform. Assoc. 29, 873–881 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xie, K., Litt, B., Roth, D. & Ellis, C. A. In Proceedings of the 21st Workshop on Biomedical Language Processing (eds Demner-Fushman, D., Cohen, K. B., Ananiadou, S. & Tsujii, J.) 369–375 (Association for Computational Linguistics, 2022).

  • Xie, K. et al. Long term epilepsy outcome dynamics revealed by natural language processing of clinic notes. Epilepsia 64, 1900–1909 (2023).

    PubMed 

    Google Scholar
     

  • van Diessen, E., van Amerongen, R. A., Zijlmans, M. & Otte, W. M. Potential merits and flaws of large language models in epilepsy care: a critical review. Epilepsia 65, 873–886 (2024).

    PubMed 

    Google Scholar
     

  • Ahmedt-Aristizabal, D. et al. Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: a focused survey. Epilepsia 58, 1817–1831 (2017).

    PubMed 

    Google Scholar
     

  • Ahmedt-Aristizabal, D. et al. Deep learning approaches for seizure video analysis: a review. Epilepsy Behav. 154, 109735 (2024).

    PubMed 

    Google Scholar
     

  • Peltola, J. et al. Semiautomated classification of nocturnal seizures using video recordings. Epilepsia 64, S65–S71 (2023).

    PubMed 

    Google Scholar
     

  • Rai, P. et al. Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence. Front. Neuroinform. https://doi.org/10.3389/fninf.2024.1324981 (2024).

  • Karácsony, T. et al. Novel 3D video action recognition deep learning approach for near real time epileptic seizure classification. Sci. Rep. 12, 19571 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alim-Marvasti, A. et al. Machine learning for localizing epileptogenic-zone in the temporal lobe: quantifying the value of multimodal clinical-semiology and imaging concordance. Front. Digit. Health https://doi.org/10.3389/fdgth.2021.559103 (2021).

  • Martini, M. L. et al. Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings. Sci. Rep. 11, 7482 (2021).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pérez-García, F. et al. Software tool for visualization of a probabilistic map of the epileptogenic zone from seizure semiologies. Front. Neuroinform. 16, 990859 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Alim-Marvasti, A. et al. Probabilistic landscape of seizure semiology localizing values. Brain Commun. 4, fcac130 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pereira Choupina, H. M. et al. NeuroKinect 3.0: multi-bed 3Dvideo-EEG system for epilepsy clinical motion monitoring. Stud. Health Technol. Inform. 247, 46–50 (2018).

    PubMed 

    Google Scholar
     

  • Jehi, L. et al. Development and validation of nomograms to provide individualised predictions of seizure outcomes after epilepsy surgery: a retrospective analysis. Lancet Neurol. 14, 283–290 (2015).

    PubMed 

    Google Scholar
     

  • Alim-Marvasti, A., Vakharia, V. N. & Duncan, J. S. Multimodal prognostic features of seizure freedom in epilepsy surgery. J. Neurol. Neurosurg. Psychiatry 93, 499–508 (2022).

    PubMed 

    Google Scholar
     



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