A forward-looking review of seizure prediction

Freestone, Dean R.; Karoly, Philippa J.; Cook, Mark

doi:10.1097/wco.0000000000000429

Cited by 151 publications

(201 citation statements)

References 72 publications

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“…30 Recent progress and future directions in seizure detection and forecasting have been reviewed elsewhere in greater depth. [31][32][33] Aside from EEG recordings, however, machine learning techniques have also been applied to novel data sources for seizure detection. In neonatal epilepsy, Karayiannis et al examined extremity movements in bedside video recordings, training neural networks to classify recordings as focal clonic seizures, myoclonic seizures, or nonseizure movements; after training on 120 recordings, the authors achieved seizure detection sensitivities of 85.5%-94.4% and specificities of 92.5%-97.9% (varying with the type of input data, with overall better detection of myoclonic seizures) on a matched testing set.…”

Section: Automated Seizure Detectionmentioning

confidence: 99%

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: Automated Seizure Detectionmentioning

confidence: 99%

Machine learning applications in epilepsy

2019

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“…In this work, we use the markers of critical slowing as a proxy to track the slow parameter k that, in turn, offers a biomarker for seizure susceptibility [3]. Moreover, we focus on seizure forecasting [3,27] rather than seizure prediction [2], as we do not attempt to predict the fast (noise) event that perturbs the system towards the seizure state.…”

Section: Conceptualization Of Critical Slowing In Epilepsymentioning

confidence: 99%

“…The mechanisms underlying the transition from a normal to a seizure state are currently an open question [2][3][4]. Unravelling the mechanisms underlying seizure generation could form the basis of much needed new treatment strategies, particularly for patients where existing treatments are ineffective.…”

Section: Introductionmentioning

confidence: 99%

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Critical slowing as a biomarker for seizure susceptibility

Maturana

Meisel

Dell

et al. 2019

Preprint

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words)The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system would suggest that prior to a seizure recorded brain signals may exhibit critical slowing, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we found key signatures of critical slowing prior to seizures. Signals related to a critically slowing process fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing is a reliable indicator that could be used in seizure forecasting algorithms.

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“…Interestingly, in both the 1938 Griffiths and Fox study as well as in the recent Neurovista and Neuropace studies, the complexity of detecting time patterns is discussed (Griffiths and Fox, 1938; Freestone et al, 2017; Baud et al, in press). Across the whole group, there was a lot of variability, but within an individual, seizure time patterns could be very consistent.…”

Section: Seizures Have Multi-temporal Patterns On Ultradian Circadiamentioning

confidence: 99%

Seizure Forecasting from Idea to Reality. Outcomes of the My Seizure Gauge Epilepsy Innovation Institute Workshop

Dumanis¹,

French²,

Bernard³

et al. 2017

eNeuro

101

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The Epilepsy Innovation Institute (Ei2) is a new research program of the Epilepsy Foundation designed to be an innovation incubator for epilepsy. Ei2 research areas are selected based on community surveys that ask people impacted by epilepsy what they would like researchers to focus on. In their 2016 survey, unpredictability was selected as a top issue regardless of seizure frequency or severity. In response to this need, Ei2 launched the My Seizure Gauge challenge, with the end goal of creating a personalized seizure advisory system device. Prior to moving forward, Ei2 convened a diverse group of stakeholders from people impacted by epilepsy and clinicians, to device developers and data scientists, to basic science researchers and regulators, for a state of the science assessment on seizure forecasting. From the discussions, it was clear that we are at an exciting crossroads. With the advances in bioengineering, we can utilize digital markers, wearables, and biosensors as parameters for a seizure-forecasting algorithm. There are also over a thousand individuals who have been implanted with ambulatory intracranial EEG recording devices. Pairing up peripheral measurements to brain states could identify new relationships and insights. Another key component is the heterogeneity of the relationships indicating that pooling findings across groups is suboptimal, and that data collection will need to be done on longer time scales to allow for individualization of potential seizure-forecasting algorithms.

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A forward-looking review of seizure prediction

Cited by 151 publications

References 72 publications

Machine learning applications in epilepsy

Machine learning applications in epilepsy

Critical slowing as a biomarker for seizure susceptibility

Seizure Forecasting from Idea to Reality. Outcomes of the My Seizure Gauge Epilepsy Innovation Institute Workshop

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