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November 16, 2019
in Technology

AI predicts seizures an hour earlier with 99,6% success

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AI predicts seizures an hour earlier with 99,6% success
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tags: AIEEGelectroencephalogramEpilepsy

Monitoring the epileptic seizures and predicting their arrival is the perfectly achieved goal of this artificial intelligence against epilepsy.

Gianluca Ricciodi Gianluca Riccio
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Epilepsy is a debilitating neurological condition that totally disrupts the lives of those who suffer from it and the people around them.

Over the years, technology has started to work hard to try to stem the effects of epilepsy attacks or to prevent them in some way.

Today there is a technological innovation that can offer even more precision in predicting epileptic seizures at any time of the day.

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A system of artificial intelligence manages to anticipate the onset of seizures with an accuracy rate of 99,6%, and does so up to an hour before they occur.

Developed by Hisham Daoud e Magdy Bayoumi of the University of Louisiana at Lafayette, combines EEG (electroencephalogram) technology and a prediction model based on machine learning.

Early versions of this system used the two techniques in separate steps: today everything happens in parallel, making the forecasting process much less cumbersome.

A phase of initial tests to monitor epilepsy and its activity

By combining the analysis and classification processes into a single automated flow, Daoud and Bayoumi were able to obtain faster and more accurate predictions.

This system can be of great help to epileptic patients who in many cases can control seizures through medication.

Laboratory tests

The researchers tested their system on 22 patients at Boston Children's Hospital. To obtain this result, the system must be “calibrated” each time on the individual patient by carrying out an initial monitoring period. The suitable places? Those outside the clinics: home and work, where most epileptic attacks can occur. EEG electrodes already on the market can be safely used.

The team is now working on a custom chip to work out the algorithms. "We are dedicating ourselves to the design of an efficient device that uses this algorithm, which has the optimal size, consumption and speed for home use," added Daoud.

The seizure system was described in a study published in the journal IEEE Transactions on Biomedical Circuits and Systems.

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The author

Gianluca Riccio, copywriter and journalist - Born in 1975, he is the creative director of an advertising agency, he is affiliated with the Italian Institute for the Future, World Future Society and H +, Network of Italian Transhumanists.

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