Predicting epileptic seizures in advance is possible: Research
The research suggests this could be particularly valuable for patients with types of epilepsy
The research suggests this could be particularly valuable for patients with types of epilepsy that are currently very difficult or impossible to treat and where the only available option is to minimize the effects of the seizure.
The research by Professor David Corne of the Intelligent Systems Lab at Heriot-Watt University’s Department of Computer Science and PHD student, Negin Moghim (published in PLOS ONE) used all the available Electroencephalography (EEG) data from an existing dataset [see note 1] and modelled it in ways never done before.
The results of their research indicate that by using predictive mathematical modelling, it is now possible to give up to 20 minutes’ advance prediction of a seizure with considerable accuracy, in most patients.
Unlike earlier EEG analysis-based studies, where they tested their approach on one or two patients, the Heriot-Watt team tested their work on the datasets of 21 patients, all with types of epilepsy that are difficult or impossible to treat at the moment.
Epilepsy affects some 50 million people worldwide and while it can be managed in some patients using prescription drugs, the remainder are likely to have a relapse after the initial remission and some may even develop drug resistant epilepsy.
Patients with uncontrolled epilepsy can be suffer accidents or even death as well as a multitude of side effects such as memory loss, depression and other psychological disorders.
Speaking about his research Professor Corne said, “EEG datasets are increasingly being generated from scientific studies and made available to all. Such 'big data' that relates human activity to neural, neuromuscular or physiological signals, becomes useful when the right mix of advanced mathematics and computing is applied.
“Building predictive models from such data, gives rise to many exciting possibilities, both in terms of applications and by providing insight into how the brain works including predicting future mental or physical states some time before they occur.
“This could relate to both intentional events, such as predicting when a person who is standing still is about to start walking and unintentional events, for example predicting when someone is about to suffer an epileptic seizure.”
With the wide use of digital EEG recording tools, these types of data are becoming more accessible for electronic manipulation. While EEGs were formerly used as a diagnosis and treatment specification tool , access to the digitised form of this information has helped generate new fields of research, from neonatal seizure detection to understanding how seizures unfolds in the epileptic brain.
Professor Corne commented “Being able to predict seizures and coupling this information with state of the art medical device technology, we should soon be able to provide unobtrusive wearable devices that provide accurate advance warning of seizures and allows patients to take prompt action to minimise the risk to themselves.”
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