TMS combined with EEG in genetic generalized epilepsy: A phase II diagnostic accuracy study

Kimiskidis, Vasilios Κ.; Tsimpiris, Alkiviadis; Ryvlin, Philippe; Kälviäinen, Reetta; Koutroumanidis, Michalis; Valentin, Antonio; Laskaris, Nikolaos A.; Kugiumtzis, Dimitris

doi:10.1016/j.clinph.2016.11.013

Cited by 44 publications

(34 citation statements)

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“…Cohen et al instead examined clinic visit notes of 200 pediatric patients (half undergoing epilepsy surgery) to predict surgical candidacy using Naïve Bayes and SVM classifiers, finding comparable performance to a panel of four neurologists up to several months before actual referral; notably, however, this comparison was limited by high measures of dispersion around the mean predictions . In contrast, Kimiskidis et al examined features derived from paired‐pulse transcranial magnetic stimulation‐EEG recordings using a Naïve Bayes classifier, achieving a mean sensitivity of 86% and specificity of 82% in distinguishing patients with genetic generalized epilepsies from controls, as well as a mean sensitivity of 80% and specificity of 73% in predicting seizure freedom at 12 months of follow‐up . More recently, An et al compared machine learning algorithms for prediction of drug‐resistant epilepsy (defined as requiring more than three medication changes during the study period) utilizing comprehensive U.S. claims data from 2006 to 2015.…”

Section: Applications In Medical Management Of Epilepsymentioning

confidence: 99%

“…86 In contrast, Kimiskidis et al examined features derived from paired-pulse transcranial magnetic stimulation-EEG recordings using a Naïve Bayes classifier, achieving a mean sensitivity of 86% and specificity of 82% in distinguishing patients with genetic generalized epilepsies from controls, as well as a mean sensitivity of 80% and specificity of 73% in predicting seizure freedom at 12 months of follow-up. 87 More recently, An et al compared machine learning algorithms for prediction of drug-resistant epilepsy (defined as requiring more than three medication changes during the study period) utilizing comprehensive U.S. claims data from 2006 to 2015. The authors found that the best-performing algorithm, a random forest classifier trained using 635 features (comprising demographic variables, comorbidities, treatment regimens, insurance data, and clinical encounters) from 175 735 records, yielded an AUC of 76.4% and could identify patients with drug-resistant epilepsy an average of 1.97 years before failing a second medication trial, using data available at the time of the first medication prescription.…”

Section: Management Of Epilepsymentioning

confidence: 99%

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Machine learning applications in epilepsy

2019

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Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre‐surgical planning, prediction of medication response, and prediction of medical and surgical outcomes using a wide variety of data sources. A brief overview of commonly used machine learning approaches, as well as challenges in further application of machine learning techniques in epilepsy, is also presented. With increasing computational capabilities, availability of effective machine learning algorithms, and accumulation of larger datasets, clinicians and researchers will increasingly benefit from familiarity with these techniques and the significant progress already made in their application in epilepsy.

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Section: Applications In Medical Management Of Epilepsymentioning

confidence: 99%

Section: Management Of Epilepsymentioning

confidence: 99%

Machine learning applications in epilepsy

2019

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“…У детей с СДВГ компонента N100 была меньше по амплитуде, чем у здоровых детей в контрольной группе [65]. Активно изучаются возможности ТМС-ЭЭГ при эпилепсии для иденти-фикации эпилептогенной зоны, предсказания эффек-тивности антиэпилептических препаратов [40,67] и эффективности инвазивной нейромодуляторной терапии [59,66,68].…”

Section: том 7 Volunclassified

Transcranial magnetic stimulation with electroencephalography: methodology, applications for research and cilinics

Nazarova

Blagoveschenskiy

Nikulin

et al. 2017

Neuromuscular Diseases

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Combined use of transcranial magnetic stimulation and electroencephalography (TMS-EEG) is a highly informative cutting edge technology which is relevant for fundamental, clinical and translational research. Unique capabilities of TMS-EEG approach allow to assess the functional state and connectivity of brain regions thus opening new prospects for the evaluation of the TMS effects in non-motor cortical areas. TMS-EEG responses have diagnostic and prognostic potential for many neurological and mental illnesses. Simultaneous co-registration of TMS with EEG remains a technically sophisticated procedure and requires specialized equipment in conjunction with application of complex data analysis techniques. This review describes the details of TMS-EEG technique, principles of the experiment design, the shape and the reproducibility of TMS-evoked responses and applications of this promising approach both in research and in clinics. Key words: transcranial magnetic stimulation with electroencephalography, functional state, connectivity of brain regions, non-motor cortical areas, evoked motor responseВведение Самый простой способ количественной оценки реактивности мозга в ответ на транскраниальную маг-нитную стимуляцию (ТМС) двигательной коры сегодня основан на оценке параметров вызванного моторного ответа (ВМО), регистрируемого с мышцы-мишени накожными электродами для электромиографии (ЭМГ). При стимуляции немоторных областей коры используются опрос испытуемого о возникающих ощу-щениях (например, при ТМС зрительной коры о нали-чии явления фосфенеза) и оценка поведенческих реак-ций (время реакции, ошибки при назывании слов и т. д.). В случае стимуляции моторной коры и регистра-ции ответа с мышцы-мишени порог ВМО можно ис-пользовать лишь для косвенной оценки состояния мозга, так как параметры ВМО зависят от всех уровней двигательной системы -от коры до мышцы. В связи с этим актуальны подходы совмещения ТМС с другими

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“…Although both studies show differences in the N100 component between patients and controls, the N100 was decreased in EPM1 patients and increased in JME patients. A recent study failed to find significant differences in the TEP between genetic generalized epilepsy patients and healthy controls (Kimiskidis et al 2017). When stimulating other sites than the motor cortex, focal epilepsy patients showed an increase in late activity (300 to 1000 ms after TMS) (Shafi et al 2015;.…”

Section: Tms In Epilepsymentioning

confidence: 99%

“…Following a previous study that showed late rhythmic activity after spTMS in 11 out of 15 epilepsy patients but not in healthy controls , recently another study was published where epileptic discharges could be induced by ppTMS in 6 out of 12 refractory epilepsy patients but not in 12 well-controlled epilepsy patients or healthy controls (Kimiskidis et al 2017). Again, both studies were biased by AED use.…”

Section: Tms-eeg In Epilepsymentioning

confidence: 99%

TMS-EEG: first steps towards a clinical application in epilepsy

Braack¹,

Esther²

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1) A parameter that is robust only when using a very strict measuring protocol, cannot be used in clinical practice2) Complete understanding of the TEP is not necessary for clinical utility 3) A silent TMS coil is required for optimal N100/P180 evaluation 4) Defining TMS as a non-invasive technique is inexplicable5) The search for significant differences has nothing to do with clinically relevant findings 6) Progress would be faster if scientists would share more with each other 7) Technology develops faster than our ability to adequately use it 8) There is an important task for the Technical Physician in the medical device industry 9) Life is what happens to us, while we are making other plans (Allen Saunders)Esther ter Braack 6 June 2018 StellingenBehorende bij het proefschrift TMS-EEG: first steps towards a clinical application in epilepsy 1) Een meetrespons die alleen robuust is met een zeer sterk gecontroleerd meetprotocol, is niet bruikbaar in de klinische praktijk 2) Volledig begrip van de TEP is niet vereist voor klinisch gebruik 3) Een stille TMS spoel is nodig voor optimale N100/P180 evaluatie 4) TMS definiëren als een niet-invasieve techniek is niet uit te leggen 5) De zoektocht naar significante verschillen heeft niets te maken met klinisch relevante resultaten 6) De vooruitgang zou sneller gaan als wetenschappers meer met elkaar zouden delen 7) Technologie ontwikkelt zich sneller dan ons vermogen om deze adequaat te gebruiken 8) Er ligt een belangrijke taak voor de Technisch Geneeskundige in de medische technologie industrie 9) Het leven is wat ons overkomt, terwijl we andere plannen maken (Allen Saunders)Esther ter Braack 6 juni 2018 In all patients, evaluation of AED success is based on the absence or recurrence of the seizures, so during this trial and error based process additional seizures may occur. It can take up to several months to either achieve an effective dosage and/or combination of AEDs, meaning that the patient becomes seizure free; or conclude that the patient suffers from refractory epilepsy. Hence, a major challenge is to shorten the time needed to evaluate the success of AEDs on a single subject level. TMS-EEG: FIRST STEPS TOWARDS A CLINICAL APPLICATION IN EPILEPSY ESTHER M. TER BRAACKIn patients with only one seizure, and a normal EEG and MRI, uncertainty remains. A seizure is a traumatic experience, and an epilepsy diagnosis has potential serious consequences, such as the loss of a driving licence or the need for career changes.Currently there is only one option in these patients: wait if a second seizure occurs. The estimated probability of seizure recurrence in patients with a normal EEG is 27.4% (Krumholz et al. 2007). This means that more than a quarter of these patients do have epilepsy, but the definite diagnosis is made only at a later stage. Therefore, another major challenge is to improve the diagnostic process in patients who present with a single seizure and a normal EEG and MRI scan.When we consider these two major challenges, namely improving diagnostics and short...

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TMS combined with EEG in genetic generalized epilepsy: A phase II diagnostic accuracy study

Cited by 44 publications

References 45 publications

Machine learning applications in epilepsy

Machine learning applications in epilepsy

Transcranial magnetic stimulation with electroencephalography: methodology, applications for research and cilinics

TMS-EEG: first steps towards a clinical application in epilepsy

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