OBJECTIVE: Conventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defining a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs are uniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data characteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, and selection of pre-ictal training data segments to reflect this could improve algorithm performance. METHODS: We propose a semi-supervised technique to select pre-ictal training data most distinguishable from interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchical clustering to identify optimal pre-ictal data epochs. RESULTS: In this paper we compare the performance of a seizure forecasting algorithm with and without hierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturally occurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p<0.001, n=6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs. CONCLUSION: Hierarchical clustering is a helpful method for training data selection overall, but should be evaluated on a subject-wise basis. SIGNIFICANCE: The clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management.

Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Prague Czech Republic

Department of Neurologic Surgery Mayo Clinic Rochester MN USA

Department of Physiology and Biomedical Engineering Mayo Clinic Rochester MN USA

Department of Veterinary Clinical Sciences University of Minnesota College of Veterinary Medicine St Paul MN USA

Institute of Biomedical Engineering University of Oxford Oxford OX3 7DQ UK

Mayo Systems Electrophysiology Laboratory Department of Neurology Mayo Clinic Rochester MN USA

Veterinary Medical Teaching Hospital University of California at Davis Davis CA 95616 USA

Zobrazit více v PubMed

Kuhlmann L, Karoly P, Freestone DR, Brinkmann BH, Temko A, Barachant A, Li F, Titericz G Jr., Lang BW, Lavery D, Roman K, Broadhead D, Dobson S, Jones G, Tang Q, Ivanenko I, Panichev O, Proix T, Nahlik M, Grunberg DB, Reuben C, Worrell G, Litt B, Liley DTJ, Grayden DB, Cook MJ, Epilepsyecosystem.org: crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG, Brain, 141 (2018) 2619–2630. PubMed PMC

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

Schulze-Bonhage A, Sales F, Wagner K, Teotonio R, Carius A, Schelle A, Ihle M, Views of patients with epilepsy on seizure prediction devices, Epilepsy Behav, 18 (2010) 388–396. PubMed

Elger CE, Mormann F, Seizure prediction and documentation—two important problems, The Lancet Neurology, 12 (2013) 531–532. PubMed

Cook MJ, O'Brien TJ, Berkovic SF, Murphy M, Morokoff A, Fabinyi G, D'Souza W, Yerra R, Archer J, Litewka L, Hosking S, Lightfoot P, Ruedebusch V, Sheffield WD, Snyder D, Leyde K, Himes D, 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 (2013) 563–571. PubMed

Davis KA, Sturges BK, Vite CH, Ruedebusch V, Worrell G, Gardner AB, Leyde K, Sheffield WD, Litt B, A novel implanted device to wirelessly record and analyze continuous intracranial canine EEG, Epilepsy Res, 96 (2011) 116–122. PubMed PMC

Howbert JJ, Patterson EE, Stead SM, Brinkmann B, Vasoli V, Crepeau D, Vite CH, Sturges B, Ruedebusch V, Mavoori J, Leyde K, Sheffield WD, Litt B, Worrell GA, Forecasting seizures in dogs with naturally occurring epilepsy, PLoS One, 9 (2014) e81920. PubMed PMC

Stanslaski S, Herron J, Chouinard T, Bourget D, Isaacson B, Kremen V, Opri E, Drew W, Brinkmann B, Gunduz A, Adamski T, Worrell G, Denison T, A Chronically-Implantable Neural Coprocessor for Investigating the Treatment of Neurological Disorders, IEEE Transactions on Biomedical Circuits and Systems, (2018). PubMed PMC

Kremen V, Brinkmann BH, Kim I, Chang S-Y, Van Gompel JJ, Herron JA, Baldassano S, Patterson EE, Litt B, Denison T, Continuous active probing and modulation of neural networks with a wireless implantable system, Biomedical Circuits and Systems Conference (BioCAS), 2017 IEEE, IEEE, 2017, pp. 1–4.

Haut SR, Hall CB, Borkowski T, Tennen H, Lipton RB, Clinical features of the pre-ictal state: mood changes and premonitory symptoms, Epilepsy & Behavior, 23 (2012) 415–421. PubMed

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

Quigg M, Circadian rhythms: interactions with seizures and epilepsy, Epilepsy Res, 42 (2000) 43–55. PubMed

Kremen V, Brinkmann BH, Kim I, Guragain H, Nasseri M, Magee AL, Pal Attia T, Nejedly P, Sladky V, Nelson N, Chang SY, Herron JA, Adamski T, Baldassano S, Cimbalnik J, Vasoli V, Fehrmann E, Chouinard T, Patterson EE, Litt B, Stead M, Van Gompel J, Sturges BK, Jo HJ, Crowe CM, Denison T, Worrell GA, Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System, IEEE J Transl Eng Health Med, 6 (2018) 2500112. PubMed PMC

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

Brinkmann BH, Patterson EE, Vite C, Vasoli VM, Crepeau D, Stead M, Howbert JJ, Cherkassky V, Wagenaar JB, Litt B, Worrell GA, Forecasting Seizures Using Intracranial EEG Measures and SVM in Naturally Occurring Canine Epilepsy, PLoS One, 10 (2015) e0133900. PubMed PMC

Varatharajah Y, Iyer RK, Berry BM, Worrell GA, Brinkmann BH, Seizure Forecasting and the Preictal State in Canine Epilepsy, Int J Neural Syst, 27 (2017) 1650046. PubMed PMC

Assi EB, Nguyen DK, Rihana S, Sawan M, Towards accurate prediction of epileptic seizures: A review, Biomedical Signal Processing and Control, 34 (2017) 144–157.

Karoly PJ, Ung H, Grayden DB, Kuhlmann L, Leyde K, Cook MJ, Freestone DR, The circadian profile of epilepsy improves seizure forecasting, Brain, 140 (2017) 2169–2182. PubMed

Korshunova I, Kindermans PJ, Degrave J, Verhoeven T, Brinkmann BH, Dambre J, Towards Improved Design and Evaluation of Epileptic Seizure Predictors, IEEE Trans Biomed Eng, 65 (2018) 502–510. PubMed

Kiral-Komek I, Roy S, Nurse E, Mashford B, Karoly P, Carroll T, Payne D, Saha S, Baldassano S, O'Brien T, Grayden D, Cook M, Freestone D, Harrer S, Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System, EBioMedicine, 27 (2018) 103–111. PubMed PMC

Brinkmann BH, Bower MR, Stengel KA, Worrell GA, Stead M, Large-scale electrophysiology: acquisition, compression, encryption, and storage of big data, Journal of neuroscience methods, 180 (2009) 185–192. PubMed PMC

Ung H, Davis KA, Wulsin D, Wagenaar J, Fox E, McDonnell JJ, Patterson N, Vite CH, Worrell G, Litt B, Temporal behavior of seizures and interictal bursts in prolonged intracranial recordings from epileptic canines, Epilepsia, 57 (2016) 1949–1957. PubMed PMC

Haut SR, Lipton RB, LeValley AJ, Hall CB, Shinnar S, Identifying seizure clusters in patients with epilepsy, Neurology, 65 (2005) 1313–1315. PubMed PMC

Karoly PJ, Nurse ES, Freestone DR, Ung H, Cook MJ, Boston R, Bursts of seizures in long-term recordings of human focal epilepsy, Epilepsia, 58 (2017) 363–372. PubMed PMC

Susmakova K, Krakovska A, Discrimination ability of individual measures used in sleep stages classification, Artif Intell Med, 44 (2008) 261–277. PubMed

Fred AL, Leitão JM, A new cluster isolation criterion based on dissimilarity increments, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25 (2003) 944–958.

Ung H, Baldassano SN, Bink H, Krieger AM, Williams S, Vitale F, Wu C, Freestone D, Nurse E, Leyde K, Davis KA, Cook M, Litt B, Intracranial EEG fluctuates over months after implanting electrodes in human brain, J Neural Eng, 14 (2017) 056011. PubMed PMC

Snyder DE, Echauz J, Grimes DB, Litt B, The statistics of a practical seizure warning system, J Neural Eng, 5 (2008) 392–401. PubMed PMC

Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation