Israeli researchers develop advanced warning device for epilepsy
Dubbed the Epiness, the device is wearable and can generate an advanced warning about an oncoming seizure, which is sent to a smartphone up to an hour prior to its onset
By ILANIT CHERNICK
In a first, researchers at Ben-Gurion University of the Negev (BGU) have developed a device for detecting and predicting epileptic seizures based on proprietary machine-learning algorithms.
Dubbed the Epiness, the device is wearable and can generate an advanced warning about an oncoming seizure, which is sent to a smartphone up to an hour prior to its onset.
Dr. Oren Shriki of the Department of Cognitive and Brain Sciences at BGU explained that “epileptic seizures expose epilepsy patients to various preventable hazards, including falls, burns and other injuries.”
Epilepsy is a highly pervasive, and at times debilitating neural disease. The BGU team explained that some 30% of patients do not adequately respond to anti-epileptic drugs and live under constant fear of possible seizures.
“For such patients,” the researchers said, “a viable seizure prediction device could offer a substantial improvement in the quality of life, enabling them to avoid seizure-related injuries.
Shriki explained that at this stage there are “unfortunately… no seizure-predicting devices that can alert patients and allow them to prepare for upcoming seizures.
“We are therefore very excited that the machine-learning algorithms that we developed enable accurate prediction of impending seizures up to one hour prior to their occurrence,” Shriki continued. “Since we have also shown that our algorithms enable a significant reduction in the number of necessary EEG electrodes, the device we are developing is both accurate and user friendly. We are currently developing a prototype that will be assessed in clinical trials later this year.”
Addressing how it works, Shriki said that the Epiness is based on a new, ground-breaking combination of EEG-based monitoring of brain activity together with proprietary machine-learning algorithms.
The device combines a wearable EEG device with state-of-the-art software that minimizes the number of necessary EEG electrodes and optimizes electrode placement on the scalp.
The machine-learning algorithms are designed to filter out noise that is not related to brain activity and extract informative measures of the underlying brain dynamics.
It distinguishes between brain activity prior to an expected epileptic seizure and normal brain activity.
“The new algorithm was developed and tested using EEG data from a large dataset of people with epilepsy that were monitored for several days prior to surgery,” the researchers said in a statement.
The patient data was then divided into short segments that were either pre-seizure or inter-ictal, which means abnormal neuronal discharges that take place between epileptic seizures.
“Several machine learning algorithms with differing complexities were trained on pre-allocated training data, which comprised 80% of the initial EEG data, and their prediction performance, as well as electrode-dependent performance, was assessed on the remaining 20% of the data,” the team said. “The algorithm with the best prediction performance reached a 97% level of accuracy, with a near-optimal performance maintained even with relatively few electrodes.”
The system was out-licensed for further development and commercialization to NeuroHelp, a startup company that was recently founded by BGN Technologies, the technology transfer company of BGU of which Shriki is the scientific founder.
According to NeuroHelp chairperson Dr. Hadar Ron, “an accurate, easy to use seizure predicting device is a highly necessary” because “current seizure alert devices can detect seizures while they are happening, and most of them depend on changes in movement, such as muscle spasms or falls.”
With up to 30% of epilepsy patients not adequately controlled by medication, Epiness “allows the patients and their caretakers to take precautionary actions and prevent injuries.
“It is also the only device that is based on brain activity rather than muscle movements or heart rate,” Ron pointed out. “We are confident that Epiness will be a valuable tool in the management of drug-resistant epilepsy.”
BGN Technologies CEO Josh Peleg highlighted that “NeuroHelp was recently founded as part of BGU’s Oazis accelerator, which was formed by the Yazamut360 entrepreneurship center of BGU.”
Its aim, he said is “to further develop and commercialize their innovative solution for the benefit of people suffering from epilepsy.”
Earlier this month, NeuroHelp won first prize in the SiliconNegev startup competition.
“[This is] an important recognition of the outstanding potential of this technology, which is based on a unique combination of brain research and artificial intelligence know-how developed at Dr. Shriki’s laboratory,” Peleg concluded.
*Featured Image: Dr. Oren Shriki, the Department of Cognitive and Brain Sciences at Ben-Gurion University. (Photo credit: Dani Machlis)