Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction

Shahbazi M, Aghajan H (2018) A generalizable model for seizure prediction based on deep learning using cnn-lstm architecture. In: 2018 IEEE Global conference on signal and information processing (GlobalSIP), Anaheim, USA

Beghi E, Giussani G (2018) Aging and the epidemiology of epilepsy. Neuroepidemiology 51:216–223

Litt B, Echauz J (2002) Prediction of epileptic seizures. Neurology 1:22–30

Nguyen R, T’ellez Zenteno JF (2009) Injuries in epilepsy: a review of its prevalence, risk factors, type of injuries and prevention. Neurol Int 1

Ridsdale L, Charlton J, Ashworth M, Richardson MP, Gulliford MC (2011) Epilepsy mortality and risk factors for death in epilepsy: a population-based study. Br J Gen Pract 61:271–278

Fisher RS (2000) Epilepsy from the patient’s perspective: Review of results of a community-based survey. Epilepsy Behav 1:9–14

Kuhlmann L, Lehnertz K, Richardson MP, Schelter B, Zaveri HP (2018) Seizure prediction — ready for a new era. Nat Rev Neurol 14:618–630

Stacey W, Le Van Quyen M, Mormann F, Schulze-Bonhage A (2011) What is the present-day eeg evidence for a preictal state? Epilepsy Res 97:243–251

Brinkamann BH, Wagenaar J, Abbot D, Adkins P, Bosshard SC, Chen M, Tieng QM, He J, Muñoz-Almaraz FJ, Botella-Rocamora P, Pardo J, Zamora-Martinez F, Hills M, Wu W, Korshunova I, Cukierski W, Vite C, Patterson EE, Litt B, Worrel GA (2016) Crowdsourcing reproducible seizure forecasting in human and canine epilepsy. Brain 139:1713–1722

Bandarabadi M, Rasekhi J, Teixeira CA, Karami MR, Dourado A (2015) On the proper selection of preictal period for seizure prediction. Epilepsy Behav 158–166:46

Giannakakis G, Sakkalis V, Pediaditis M, Tsiknakis M (2014) Methods for seizure detection and prediction: An overview. Mod Electroencephalographic Assem Tech 91:131–157

Georgis-Yap Z, Popovic MR, Khan SS (2022) Preictal-interictal classification for seizure prediction. In: The 35th Canadian conference on artificial intelligence

Burrello A, Cavigelli L, Schindler K, Benini L, Rahimi A (2019) Laelaps: An energy-efficient seizure detection algorithm from long-term human ieeg recordings without false alarms. In: 2019 Design, automation & test in Europe conference & exhibition (DATE). Florence, Italy

Shoeb AH (2009) Application of machine learning to epileptic seizure onset detection and treatment. PhD thesis, Massachusetts Institute of Technology

Acharya UR, Hagiwara Y, Adeli H (2018) Automated seizure prediction. Epilepsy Behav 88:251–261

Natu M, Bachute M, Gite S, Kotecha K, Vidyarthi A (2022) Review on epileptic seizure prediction: machine learning and deep learning approaches. Comput Math Methods Med 2022

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

Teixeira CA, Direito B, Bandarabadi M, Le Van Quyen M, Valerrama M, Schelter B, Schulze-Bonhage A, Navarro V, Sales F, Dourado A (2014) Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients. Comput Methods Prog Biomed 114:324–336

Fei K, Wang W, Yang Q, Tang S (2017) Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249:290–298

Mirowski P, Madhavan D, LeCun Y, Kuzniecky R (2009) Classification of patterns of eeg synchronization for seizure prediction. Clin Neurophysiol 120:1927–1940

Khan H, Marcuse L, Fields M, Swann K, Yener B (2018) Focal onset seizure prediction using convolutional networks. IEEE Trans Biomed Eng 65:2109–2118

Truong ND, Nguyen AD, Kuhlmann L, Bonyadi MR, Yang J, Ippolito S, Kavehei O (2018) Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw 105:104–111

Eberlein M, Hildebrand R, Tetzlaff R, Hoffmann N, Kuhlmann L, Brinkmann B, M”uller J (2018) Convolutional neural networks for epileptic seizure prediction. In: 2018 IEEE International conference on bioinformatics and biomedicine (BIBM), Madrid

Zhang Y, Guo Y, Yang P, Chen W, Lo B (2020) Epilepsy seizure prediction on eeg using common spatial pattern and convolutional neural network. IEEE J Biomed Health Informat 24:465–474

Liu Y, Sivathamboo S, Goodin P, Bonnington P, Kwan P, Kuhlmann L, O’Brien T, Perucca P, Ge Z (2020) Epileptic seizure detection using convolutional neural network: A multi-biosignal study. In: ACSW ’20: Proceedings of the Australasian computer science week multiconference, Melbourne, Australia

Dissanayake T, Fernando T, Denman S, Sridharan S, Fookes C (2021) Deep learning for patient-independent epileptic seizure prediction using scalp eeg signals. IEEE Sensors J 21:9377–9388

Jana R, Mukherjee I (2021) Deep learning based efficient epileptic seizure prediction with eeg channel optimization. Biomed Signal Process Control 68

Xu Y, Yang J, Zhao S, Wu H, Sawan M (2020) An end-to-end deep learning approach for epileptic seizure prediction. In: 2020 2nd IEEE International conference on artificial intelligence circuits and systems (AICAS), Italy

Tsiouris KM, Pezoulas VC, Zervakis M, Konitsiotis S, Koutsouris DD, Fotiadis DI (2018) A long short-term memory deep learning network for the prediction of epileptic seizures using eeg signals. Comput Biol Med 99:24–37

Abdelhameed AM, Bayoumi M (2018) Semi-supervised deep learning system for epileptic seizures onset prediction. In: 2018 17th IEEE International conference on machine learning and applications (ICMLA), Orlando

Daoud H, Bayoumi MA (2019) Efficient epileptic seizure prediction based on deep learning. IEEE Trans Biomed Circ Syst 13:804–813

Wei X, Zhou L, Zhang Z, Chen Z, Zhou Y (2019) Early prediction of epileptic seizures using a long-term recurrent convolutional network. J Neurosci Methods 327

Usman SM, Khalid S, Aslam MH (2020) Epileptic seizures prediction using deep learning techniques. IEEE Access 8:39998–40007

Hussein A, Djandji M, Mahmoud R, Dhaybi M, Hajj HM (2020) Augmenting dl with adversarial training for robust prediction of epilepsy seizures. J ACM 1

Prathaban BP, Balasubramanian R (2021) Dynamic learning framework for epileptic seizure prediction using sparsity based eeg reconstruction with optimized cnn classifier. Expert Syst Appl 170

Ozcan AR, Erturk S (2019) Seizure prediction in scalp eeg using 3d convolutional neural networks with an image-based approach. IEEE Trans Neural Syst Rehab Eng 27:2284–2293

Truong ND, Zhou L, Kavehei O (2019) Semi-supervised seizure prediction with generative adversarial networks. In: 2019 41st Annual international conference of the ieee engineering in medicine and biology society (EMBC), Berlin

Sherstinsky A (2020) Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Physic D Nonlinear Phenom 404:132306

Thill M, Konen W, Wang H, B”ack T, (2021) Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Appl Soft Comput 112:107751

Stefan Denkovski SSK, Mihailidis A (2023) Temporal shift - multi-objective loss function for improved anomaly fall detection. In: $$15^{th}$$ Asian conference on machine learning

Khan SS, Khoshbakhtian F, Ashraf AB (2021) Anomaly detection approach to identify early cases in a pandemic using chest x-rays. In: Canadian conference on AI

Jacob Nogas SSK, Mihailidis A (2018) Fall detection from thermal camera using convolutional lstm autoencoder. In: Proceedings of the 2nd workshop on aging, rehabilitation and independent assisted living, IJCAI workshop

Abedi A, Khan SS (2023) Detecting disengagement in virtual learning as an anomaly using temporal convolutional network autoencoder. SIViP

Al-Fahoum AS, Al-Fraihat AA (2014) Methods of eeg signal features extraction using linear analysis in frequency and time-frequency domains. Int Scholar Res Not 2014

Herrmann CS, Rach S, Vosskuhl J, Str’’uber D (2014) Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topogr 27:438–450

Herrmann CS, Rach S, Vosskuhl J, Str’’uber D (2014) Time–frequency analysis of event-related potentials: A brief tutorial. Brain Topogr 27:438–450

Fadzal CWNFCW, Mansor W, Khuan LY, Zabidi A (2012) Short-time fourier transform analysis of eeg signal from writing. In: 2012 IEEE 8th International colloquium on signal processing and its applications. Malacca, Malaysia

Edakawa K, Yanagisawa T, Kishima H, Fukuma R, Oshino S, Khoo HM, Kobayashi M, Tanaka M, Yoshimine T (2016) Detection of epileptic seizures using phase–amplitude coupling in intracranial electroencephalography. Sci Rep 6(1):1–8

Huang J, Ling CX (2005) Using auc and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng 17:299–310

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tajani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: An imperative style, high-performance deep learning library. In: Advances in neural information processing systems 32, Vancouver

Branco P, Torgo L, Ribeiro RR (2016) A survey of predictive modeling on imbalanced domains. ACM Comput Surv (CSUR) 49:1–50

Khan SS, Karg ME, Kulić D, Hoey J (2017) Detecting falls with x-factor hidden markov models. Appl Soft Comput 55:168–177

Khan SS, Mishra PK, Ye B, Newman K, Iaboni A, Mihailidis A (2023) Empirical thresholding on spatio-temporal autoencoders trained on surveillance videos in a dementia care unit. In: 2023 20th Conference on robots and vision (CRV), pp 265–272. IEEE

Habashi AG, Azab AM, Eldawlatly S, Aly GM (2023) Generative adversarial networks in eeg analysis: an overview. J NeuroEng Rehab 20(1):40

Khan SS, Nogas J, Mihailidis A (2021) Spatio-temporal adversarial learning for detecting unseen falls. Pattern Anal Applic 24:381–391