Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy.
Advisory Committees
Big Data
Biological Ontologies
Biomedical Research
Brain
/ diagnostic imaging
Common Data Elements
Computer Security
Confidentiality
Deep Learning
Electrocorticography
Electronic Health Records
Epilepsy
/ diagnostic imaging
Genomics
Humans
Information Dissemination
Neuroimaging
Research Support as Topic
Smartphone
Societies, Medical
Stakeholder Participation
Telemedicine
Wearable Electronic Devices
big data
epilepsy
epilepsy informatics
epilepsy ontology
Journal
Epilepsia
ISSN: 1528-1167
Titre abrégé: Epilepsia
Pays: United States
ID NLM: 2983306R
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
25
10
2019
revised:
07
07
2020
accepted:
08
07
2020
pubmed:
9
8
2020
medline:
26
1
2021
entrez:
9
8
2020
Statut:
ppublish
Résumé
Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1869-1883Subventions
Organisme : Medical Research Council
ID : MR/K006584/1
Pays : United Kingdom
Informations de copyright
© 2020 International League Against Epilepsy.
Références
Lohr S. The origins of "big data": an etymological detective story. New York Times. February 1, 2013.
Laney D. 3D Data Management: Controlling Data Volume, Velocity, and Variety, Technical report, META Group. 2001
Lhatoo S, Noebels J, Whittemore V. Sudden unexpected death in epilepsy: identifying risk and preventing mortality. Epilepsia. 2015;56:1700-6.
Boat TF, Adamson PC, Asbury C. Elements of an Integrated National Strategy to Accelerate Research and Product Development for Rare Diseases. Washington, DC: Institute of Medicine; 2010.
Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data. 2016;3:160018.
Zhang GQ, Cui L, Mueller R, et al. The National Sleep Research Resource: towards a sleep data commons. J Am Med Inform Assoc. 2018;25:1351-8.
Stead M, Halford JJ. Proposal for a standard format for neurophysiology data recording and exchange. J Clin Neurophysiol. 2016;33:403-13.
Kearney H, Byrne S, Cavalleri GL, Delanty N. Tackling epilepsy with high-definition precision medicine: a review. JAMA Neurol. 2019;76(9):1109.
Gymrek M, McGuire AL, Golan D, Halperin E, Erlich Y. Identifying personal genomes by surname inference. Science. 2013;339:321-4.
Clayton EW, Evans BJ, Hazel JW, Rothstein MA. The law of genetic privacy: applications, implications, and limitations. J Law Biosci. 2019;6:1-36.
Bodenreider O. Biomedical ontologies in action: role in knowledge management, data integration and decision support. Yearb Med Inform. 2008;67-79.
Haendel MA, Chute CG, Robinson PN. Classification, ontology, and precision medicine. N Engl J Med. 2018;379:1452-62.
Hitzler P, Krotszch M, Parsia B, Patel-Schneider PF, Rudolph S. OWL 2 Web ontology language primer. W3C recommendation. 2009.
Berg AT, Berkovic SF, Brodie MJ, et al. Revised terminology and concepts for organization of seizures and epilepsies: report of the ILAE Commission on Classification and Terminology, 2005-2009. Epilepsia. 2010;51:676-85.
Berg AT, Cross JH. Towards a modern classification of the epilepsies? Lancet Neurol. 2010;9:459-61.
Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25:25-9.
Sahoo SS, Lhatoo SD, Gupta DK, et al. Epilepsy and seizure ontology: towards an epilepsy informatics infrastructure for clinical research and patient care. J Am Med Inform Assoc. 2014;21:82-9.
Cui L, Bozorgi A, Lhatoo SD, Zhang GQ, Sahoo SS. EpiDEA: extracting structured epilepsy and seizure information from patient discharge summaries for cohort identification. AMIA Annu Symp Proc. 2012;2012:1191-200.
Cui L, Huang Y, Tao S, Lhatoo SD, Zhang GQ. ODaCCI: ontology-guided data curation for multisite clinical research data integration in the NINDS Center for SUDEP Research. AMIA Annu Symp Proc. 2016;2016:441-50.
Cui L, Sahoo SS, Lhatoo SD, et al. Complex epilepsy phenotype extraction from narrative clinical discharge summaries. J Biomed Inform. 2014;51:272-9.
Jayapandian C, Wei A, Ramesh P, et al. A scalable neuroinformatics data flow for electrophysiological signals using MapReduce. Front Neuroinform. 2015;9:4.
Jayapandian CP, Chen CH, Bozorgi A, Lhatoo SD, Zhang GQ, Sahoo SS. Cloudwave: distributed processing of "big data" from electrophysiological recordings for epilepsy clinical research using Hadoop. AMIA Annu Symp Proc. 2013;2013:691-700.
Sahoo SS, Jayapandian C, Garg G, et al. Heart beats in the cloud: distributed analysis of electrophysiological 'Big Data' using cloud computing for epilepsy clinical research. J Am Med Inform Assoc. 2014;21:263-71.
Sahoo SS, Wei A, Valdez J, et al. NeuroPigPen: a scalable toolkit for processing electrophysiological signal data in neuroscience applications using Apache Pig. Front Neuroinform. 2016;10:18.
Sahoo SS, Zhao M, Luo L, et al. OPIC: ontology-driven patient information capturing system for epilepsy. AMIA Annu Symp Proc. 2012;2012:799-808.
Zhang GQ, Cui L, Lhatoo S, Schuele SU, Sahoo SS. MEDCIS: multi-modality epilepsy data capture and integration system. AMIA Annu Symp Proc. 2014;2014:1248-57.
Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizures. Clin Neurophysiol. 2005;116:569-87.
Schulze-Bonhage A, Feldwisch-Drentrup H, Ihle M. The role of high-quality EEG databases in the improvement and assessment of seizure prediction methods. Epilepsy Behav. 2011;22(Suppl 1):S88-93.
Cook MJ, O'Brien TJ, Berkovic SF, 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. 2013;12:563-71.
Ihle M, Feldwisch-Drentrup H, Teixeira CA, et al. EPILEPSIAE-a European epilepsy database. Comput Methods Programs Biomed. 2012;106:127-38.
Klatt J, Feldwisch-Drentrup H, Ihle M, et al. The EPILEPSIAE database: an extensive electroencephalography database of epilepsy patients. Epilepsia. 2012;53:1669-76.
Kini LG, Davis KA, Wagenaar JB. Data integration: combined imaging and electrophysiology data in the cloud. Neuroimage. 2016;124:1175-81.
Wagenaar JB, Worrell GA, Ives Z, Dumpelmann M, Litt B, Schulze-Bonhage A. Collaborating and sharing data in epilepsy research. J Clin Neurophysiol. 2015;32:235-9.
O'Regan ME, Brown JK. Abnormalities in cardiac and respiratory function observed during seizures in childhood. Dev Med Child Neurol. 2005;47:4-9.
EEG motor movement/imagery dataset. [cited 2020 July 28]. Available at: https://archive.physionet.org/pn4/eegmmidb/.
Trebaul L, Deman P, Tuysenge V, et al. Probabilistic functional tractography of the human cortex revisited. Neuroimage. 2018;181:414-29.
Lhatoo SD, Solomon JK, McEvoy AW, Kitchen ND, Shorvon SD, Sander JW. A prospective study of the requirement for and the provision of epilepsy surgery in the United Kingdom. Epilepsia. 2003;44:673-6.
Cloppenborg T, May TW, Blümcke I, et al. Trends in epilepsy surgery: stable surgical numbers despite increasing presurgical volumes. J Neurol Neurosurg Psychiatry. 2016;87:1322-9.
Blumcke I, Spreafico R, Haaker G, et al. Histopathological findings in brain tissue obtained during epilepsy surgery. N Engl J Med. 2017;377:1648-56.
Blumcke I, Spreafico R. An international consensus classification for focal cortical dysplasias. Lancet Neurol. 2011;10:26-7.
Blumcke I, Cross JH, Spreafico R. The international consensus classification for hippocampal sclerosis: an important step towards accurate prognosis. Lancet Neurol. 2013;12:844-6.
Blümcke I, Aronica E, Miyata H, et al. International recommendation for a comprehensive neuropathologic workup of epilepsy surgery brain tissue: a consensus task force report from the ILAE Commission on Diagnostic Methods. Epilepsia. 2016;57:348-58.
Dwivedi R, Ramanujam B, Chandra PS, et al. Surgery for drug-resistant epilepsy in children. N Engl J Med. 2017;377:1639-47.
Wiebe S, Blume WT, Girvin JP, Eliasziw M. A randomized, controlled trial of surgery for temporal-lobe epilepsy. N Engl J Med. 2001;345:311-8.
Whelan CD, Altmann A, Botía JA, et al. Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study. Brain. 2018;141:391-408.
Bernasconi A, Cendes F, Theodore WH, et al. Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: a consensus report from the International League Against Epilepsy Neuroimaging Task Force. Epilepsia. 2019;60:1054-68.
McGovern K, Stillman N, McKenna K, et al. The epilepsy phenome/genome project. Clin Trials. 2013;10:568-86.
Nesbitt G, McKenna K, Mays V, et al. The Epilepsy Phenome/Genome Project (EPGP) informatics platform. Int J Med Inform. 2013;82:248-59.
Epi4K Consortium. Epilepsy Phenome/Genome Project. Ultra-rare genetic variation in common epilepsies: a case-control sequencing study. Lancet Neurol. 2017;16:135-43.
Epi4K Consortium, Epilepsy Phenome/Genome Project, Allen AS, et al. De novo mutations in epileptic encephalopathies. Nature. 2013;501:217-21.
International League Against Epilepsy Consortium on Complex Epilepsies. Electronic address e-auea. Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies. Lancet Neurol. 2014;13:893-903.
EuroEPINOMICS-RES Consortium. Epilepsy Phenome/Genome Project, Epi4K Consortium. De novo mutations in synaptic transmission genes including DNM1 cause epileptic encephalopathies. Am J Hum Genet. 2017;100:179.
Mailman MD, Feolo M, Jin Y, et al. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007;39:1181-6.
KOMP Repository UD. KOMP repository knockout mouse project. [cited 2020 July 28]. Available at: https://www.komp.org/
Friedel RH, Seisenberger C, Kaloff C, Wurst W. EUCOMM--the European conditional mouse mutagenesis program. Brief Funct Genomic Proteomic. 2007;6:180-185. https://doi.org/10.1093/bfgp/elm022
NorCOMM. North American Conditional Mouse Mutagenesis Project. [cited 2020 July 28]. Available at: http://www.norcomm2.org/
Medicine Texas A&M Institute for Genomic Medicine. The world's largest collection of C57 ES cells and mice. [cited 2020 July 28]. Available at: https://www.tigm.org/
National Institutes of Health. Knockout mouse phenotyping. [cited 2020 July 28]. Available at: https://commonfund.nih.gov/komp2
Harte-Hargrove LC, French JA, Pitkanen A, Galanopoulou AS, Whittemore V, Scharfman HE. Common data elements for preclinical epilepsy research: standards for data collection and reporting. A TASK3 report of the AES/ILAE Translational Task Force of the ILAE. Epilepsia. 2017;58(Suppl 4):78-86.
Galanopoulou AS, French JA, O'Brien T, Simonato M. Harmonization in preclinical epilepsy research: a joint AES/ILAE translational initiative. Epilepsia. 2017;58(Suppl 4):7-9.
Hernan AE, Schevon CA, Worrell GA, et al. Methodological standards and functional correlates of depth in vivo electrophysiological recordings in control rodents. A TASK1-WG3 report of the AES/ILAE Translational Task Force of the ILAE. Epilepsia. 2017;58(Suppl 4):28-39.
Kadam SD, D'Ambrosio R, Duveau V, et al. Methodological standards and interpretation of video-electroencephalography in adult control rodents. A TASK1-WG1 report of the AES/ILAE Translational Task Force of the ILAE. Epilepsia. 2017;58(Suppl 4):10-27.
Moyer JT, Gnatkovsky V, Ono T, et al. Standards for data acquisition and software-based analysis of in vivo electroencephalography recordings from animals. A TASK1-WG5 report of the AES/ILAE Translational Task Force of the ILAE. Epilepsia. 2017;58(Suppl 4):53-67.
Raimondo JV, Heinemann U, de Curtis M, et al. Methodological standards for in vitro models of epilepsy and epileptic seizures. A TASK1-WG4 report of the AES/ILAE Translational Task Force of the ILAE. Epilepsia. 2017;58(Suppl 4):40-52.
Simonato M, Iyengar S, Brooks-Kayal A, et al. Identification and characterization of outcome measures reported in animal models of epilepsy: protocol for a systematic review of the literature-A TASK2 report of the AES/ILAE Translational Task Force of the ILAE. Epilepsia. 2017;58(Suppl 4):68-77.
Ben-Menachem E. Epilepsy in 2015: the year of collaborations for big data. Lancet Neurol. 2016;15:6-7.
Boshuisen K, Arzimanoglou A, Cross JH, et al. Timing of antiepileptic drug withdrawal and long-term seizure outcome after paediatric epilepsy surgery (TimeToStop): a retrospective observational study. Lancet Neurol. 2012;11:784-91.
Vale CL, Rydzewska LHM, Rovers MM, et al. Uptake of systematic reviews and meta-analyses based on individual participant data in clinical practice guidelines: descriptive study. BMJ. 2015;350:h1088.
Nevitt SJ, Marson AG, Davie B, Reynolds S, Williams L, Smith CT. Exploring changes over time and characteristics associated with data retrieval across individual participant data meta-analyses: systematic review. BMJ. 2017;357:j1390.
Nevitt SJ, Sudell M, Weston J, Tudur Smith C, Marson AG. Antiepileptic drug monotherapy for epilepsy: a network meta-analysis of individual participant data. Cochrane Database Syst Rev. 2017;12:CD011412.
Fallah A, Guyatt GH, Snead OC, et al. Predictors of seizure outcomes in children with tuberous sclerosis complex and intractable epilepsy undergoing resective epilepsy surgery: an individual participant data meta-analysis. PLoS One. 2013;8:e53565.
Offringa M, Bossuyt PMM, Lubsen J, et al. Risk factors for seizure recurrence in children with febrile seizures: a pooled analysis of individual patient data from five studies. J Pediatr. 1994;124:574-84.
Hampel KG, Thijs RD, Elger CE, Surges R. Recurrence risk of ictal asystole in epilepsy. Neurology. 2017;89:785-91.
Lamberink HJ, Otte WM, Geerts AT, et al. Individualised prediction model of seizure recurrence and long-term outcomes after withdrawal of antiepileptic drugs in seizure-free patients: a systematic review and individual participant data meta-analysis. Lancet Neurol. 2017;16:523-31.
Hemingway H, Asselbergs FW, Danesh J, et al. Big data from electronic health records for early and late translational cardiovascular research: challenges and potential. Eur Heart J. 2018;39:1481-95.
Weber GM, Mandl KD, Kohane IS. Finding the missing link for big biomedical data. JAMA. 2014;311:2479-80.
Denaxas SC, Morley KI. Big biomedical data and cardiovascular disease research: opportunities and challenges. Eur Heart J Qual Care Clin Outcomes. 2015;1:9-16.
Denaxas SC, George J, Herrett E, et al. Data resource profile: cardiovascular disease research using linked bespoke studies and electronic health records (CALIBER). Int J Epidemiol. 2012;41:1625-38.
Pathak J, Kho AN, Denny JC. Electronic health records-driven phenotyping: challenges, recent advances, and perspectives. J Am Med Inform Assoc. 2013;20:e206-e211.
Morley KI, Wallace J, Denaxas SC, et al. Defining disease phenotypes using national linked electronic health records: a case study of atrial fibrillation. PLoS One. 2014;9:e110900.
Denaxas S, Direk K, Gonzalez-Izquierdo A, et al. Methods for enhancing the reproducibility of biomedical research findings using electronic health records. BioData Min. 2017;10:31.
Levin MA, Wanderer JP, Ehrenfeld JM. Data, big data, and metadata in anesthesiology. Anesth Analg. 2015;121:1661-7.
Mooney SJ, Westreich DJ, El-Sayed AM. Commentary: Epidemiology in the era of big data. Epidemiology. 2015;26:390-4.
Holmes LB, Wyszynski DF, Lieberman E. The AED (antiepileptic drug) pregnancy registry: a 6-year experience. Arch Neurol. 2004;61:673-8.
Shorvon SD, Goodridge DM. Longitudinal cohort studies of the prognosis of epilepsy: contribution of the National General Practice Study of Epilepsy and other studies. Brain. 2013;136:3497-510.
The Lancet Neurology. EURAP signals a new era in epilepsy research. Lancet Neurol. 2011;10:591.
Reid AY, St Germaine-Smith C, Liu M, et al. Development and validation of a case definition for epilepsy for use with administrative health data. Epilepsy Res. 2012;102:173-9.
Fonferko-Shadrach B, Lacey AS, White CP, et al. Validating epilepsy diagnoses in routinely collected data. Seizure. 2017;52:195-8.
Meeraus WH, Petersen I, Chin RF, Knott F, Gilbert R. Childhood epilepsy recorded in primary care in the UK. Arch Dis Child. 2013;98:195-202.
Herrett E, Thomas SL, Schoonen WM, Smeeth L, Hall AJ. Validation and validity of diagnoses in the general practice research database: a systematic review. Br J Clin Pharmacol. 2010;69:4-14.
Shivade C, Raghavan P, Fosler-Lussier E, et al. A review of approaches to identifying patient phenotype cohorts using electronic health records. J Am Med Inform Assoc. 2014;21:221-30.
Faught E. Antiepileptic drug trials: the view from the clinic. Epileptic Disord. 2012;14:114-23.
Kim H, Thurman DJ, Durgin T, Faught E, Helmers S. Estimating epilepsy incidence and prevalence in the US pediatric population using nationwide health insurance claims data. J Child Neurol. 2016;31:743-9.
Gaitatzis A, Carroll K, Majeed A, Sander JW. The epidemiology of the comorbidity of epilepsy in the general population. Epilepsia. 2004;45:1613-22.
Hesdorffer DC, Ishihara L, Mynepalli L, Webb DJ, Weil J, Hauser WA. Epilepsy, suicidality, and psychiatric disorders: a bidirectional association. Ann Neurol. 2012;72:184-91.
Josephson CB, Lowerison M, Vallerand I, et al. Association of depression and treated depression with epilepsy and seizure outcomes: a multicohort analysis. JAMA Neurol. 2017;74:533-9.
Su CC, Chi MH, Lin SH, Yang YK. Bidirectional association between autism spectrum disorder and epilepsy in child and adolescent patients: a population-based cohort study. Eur Child Adolesc Psychiatry. 2016;25:979-87.
Sundelin HEK, Larsson H, Lichtenstein P, et al. Autism and epilepsy: a population-based nationwide cohort study. Neurology. 2016;87:192-7.
Arana A, Wentworth CE, Ayuso-Mateos JL, Arellano FM. Suicide-related events in patients treated with antiepileptic drugs. N Engl J Med. 2010;363:542-51.
Pugh MJV, Hesdorffer D, Wang C-P, et al. Temporal trends in new exposure to antiepileptic drug monotherapy and suicide-related behavior. Neurology. 2013;81:1900-6.
Josephson CB, Gonzalez-Izquierdo A, Denaxas S, et al. Serotonin reuptake inhibitors and mortality in epilepsy: a linked primary-care cohort study. Epilepsia. 2017;58:2002-9.
Ridsdale L, Charlton J, Ashworth M, Richardson MP, Gulliford MC. Epilepsy mortality and risk factors for death in epilepsy: a population-based study. Br J Gen Pract. 2011;61:e271-e278.
Kaiboriboon K, Schiltz NK, Bakaki PM, Lhatoo SD, Koroukian SM. Premature mortality in poor health and low income adults with epilepsy. Epilepsia. 2014;55:1781-8.
Devinsky O, Dilley C, Ozery-Flato M, et al. Changing the approach to treatment choice in epilepsy using big data. Epilepsy Behav. 2016;56:32-7.
Grinspan ZM, Shapiro JS, Abramson EL, Hooker G, Kaushal R, Kern LM. Predicting frequent ED use by people with epilepsy with health information exchange data. Neurology. 2015;85(12):1031-8.
Thurman DJ, Kobau R, Luo YH, Helmers SL, Zack MM. Health-care access among adults with epilepsy: the U.S. National Health Interview Survey, 2010 and 2013. Epilepsy Behav. 2016;55:184-8.
Aghaei-Lasboo A, Fisher RS. Methods for measuring seizure frequency and severity. Neurol Clin. 2016;34:383-94.
Bidwell J, Khuwatsamrit T, Askew B, Ehrenberg JA, Helmers S. Seizure reporting technologies for epilepsy treatment: a review of clinical information needs and supporting technologies. Seizure. 2015;32:109-17.
Osorio I, Schachter S. Extracerebral detection of seizures: a new era in epileptology? Epilepsy Behav. 2011;22(Suppl 1):S82-7.
van Andel J, Thijs RD, de Weerd A, Arends J, Leijten F. Non-EEG based ambulatory seizure detection designed for home use: what is available and how will it influence epilepsy care? Epilepsy Behav. 2016;57:82-9.
Van de Vel A, Verhaert K, Ceulemans B. Critical evaluation of four different seizure detection systems tested on one patient with focal and generalized tonic and clonic seizures. Epilepsy Behav. 2014;37:91-4.
Jory C, Shankar R, Coker D, McLean B, Hanna J, Newman C. Safe and sound? A systematic literature review of seizure detection methods for personal use. Seizure. 2016;36:4-15.
Krauss GL, Ryvlin P. Non-EEG seizure detection is here. Neurology. 2018;90:207-8.
Poh M-Z, Loddenkemper T, Reinsberger C, et al. Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor. Epilepsia. 2012;53:e93-e97.
Beniczky S, Polster T, Kjaer TW, Hjalgrim H. Detection of generalized tonic-clonic seizures by a wireless wrist accelerometer: a prospective, multicenter study. Epilepsia. 2013;54:e58-e61.
Narechania AP, Garic II, Sen-Gupta I, Macken MP, Gerard EE, Schuele SU. Assessment of a quasi-piezoelectric mattress monitor as a detection system for generalized convulsions. Epilepsy Behav. 2013;28:172-6.
Patterson AL, Mudigoudar B, Fulton S, et al. SmartWatch by smartmonitor: assessment of seizure detection efficacy for various seizure types in children, a large prospective single-center study. Pediatr Neurol. 2015;53:309-11.
Poppel KV, Fulton SP, McGregor A, Ellis M, Patters A, Wheless J. Prospective study of the Emfit movement monitor. J Child Neurol. 2013;28:1434-6.
Beniczky S, Conradsen I, Moldovan M, et al. Quantitative analysis of surface electromyography during epileptic and nonepileptic convulsive seizures. Epilepsia. 2014;55:1128-34.
Beniczky S, Conradsen I, Moldovan M, et al. Automated differentiation between epileptic and nonepileptic convulsive seizures. Ann Neurol. 2015;77:348-51.
Conradsen I, Moldovan M, Jennum P, Wolf P, Farina D, Beniczky S. Dynamics of muscle activation during tonic-clonic seizures. Epilepsy Res. 2013;104:84-93.
Sarkis RA, Thome-Souza S, Poh M-Z, et al. Autonomic changes following generalized tonic clonic seizures: an analysis of adult and pediatric patients with epilepsy. Epilepsy Res. 2015;115:113-8.
Cogan D, Nourani M, Harvey J, Nagaraddi V. Epileptic seizure detection using wristworn biosensors. Conf Proc IEEE Eng Med Biol Soc. 2015;2015:5086-9.
Kroll RR, Boyd JG, Maslove DM. Accuracy of a wrist-worn wearable device for monitoring heart rates in hospital inpatients: a prospective observational study. J Med Internet Res. 2016;18:e253.
Eggleston KS, Olin BD, Fisher RS. Ictal tachycardia: the head-heart connection. Seizure. 2014;23:496-505.
Zijlmans M, Flanagan D, Gotman J. Heart rate changes and ECG abnormalities during epileptic seizures: prevalence and definition of an objective clinical sign. Epilepsia. 2002;43:847-54.
Conradsen I, Beniczky S, Wolf P, Henriksen J, Sams T, Sorensen HB. Seizure onset detection based on a uni- or multi-modal intelligent seizure acquisition (UISA/MISA) system. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:3269-72.
Conradsen I, Beniczky S, Wolf P, Kjaer TW, Sams T, Sorensen HB. Automatic multi-modal intelligent seizure acquisition (MISA) system for detection of motor seizures from electromyographic data and motion data. Comput Methods Programs Biomed. 2012;107:97-110.
Conradsen I, Beniczky S, Wolf P, Terney D, Sams T, Sorensen HB. Multi-modal intelligent seizure acquisition (MISA) system-a new approach towards seizure detection based on full body motion measures. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:2591-5.
Van de Vel A, Cuppens K, Bonroy B, et al. Non-EEG seizure detection systems and potential SUDEP prevention: state of the art: review and update. Seizure. 2016;41:141-53.
Van de Vel A, Cuppens K, Bonroy B, et al. Non-EEG seizure-detection systems and potential SUDEP prevention: state of the art. Seizure. 2013;22:345-55.