Today’s Solutions: December 04, 2021

A huge difficulty for people living with epilepsy is even when medicated, unpredictable and sometimes deadly seizures can continue to happen. Fortunately, a new study by the Mayo Clinic has invented a device that may be able to give those with epilepsy more control over their lives.

The study, published in Scientific Reports, monitored a group of patients with drug-resistant epilepsy for six months. Data to confirm seizures was gathered from a neurostimulation device already implanted in their brain. The groundbreaking thing about this study is all the information used to predict a seizure was sourced from a wearable wristband. It recorded information about blood flow, heart rate, movement, electrical characteristics of the skin and body temperature. Such a non-invasive treatment has never been achieved.

To analyze the data, the team used advanced AI algorithms, finding patterns and trends to associate with the seizures. It was found that around 30 minutes before a seizure, a warning sign from the device popped up. Removing some of the limitations of unpredictable seizures gives people living with epilepsy much more control over their lives. If it is known an attack is coming, fast-acting medication can be administered or their neurostimulator can be increased to prevent or minimize it.

“Just as a reliable weather forecast helps people plan their activities, so, too, could seizure forecasting help patients living with epilepsy adjust their plans if they knew a seizure was imminent,” said Benjamin Brinkmann, senior author of the paper. “This study using a wrist-worn device shows that providing reliable seizure forecasts for people living with epilepsy is possible without directly measuring brain activity.”

This study provides the first clear evidence of seizure forecasts. Researchers hope this treatment can be applied in a clinical setting with further trials.

Source study: Scientific Reports – Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning

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