Posts Tagged micro

[WEB SITE] Researchers develop new prediction method for epileptic seizures

Epileptic seizures strike with little warning and nearly one third of people living with epilepsy are resistant to treatment that controls these attacks. More than 65 million people worldwide are living with epilepsy.

Now researchers at the University of Sydney have used advanced artificial intelligence and machine learning to develop a generalized method to predict when seizures will strike that will not require surgical implants.

Dr Omid Kavehei from the Faculty of Engineering and IT and the University of Sydney Nano Institute said: “We are on track to develop an affordable, portable and non-surgical device that will give reliable prediction of seizures for people living with treatment-resistant epilepsy.”

In a paper published this month in Neural Networks, Dr Kavehei and his team have proposed a generalized, patient-specific, seizure-prediction method that can alert epilepsy sufferers within 30 minutes of the likelihood of a seizure.

Dr Kavehei said there had been remarkable advances in artificial intelligence as well as micro- and nano-electronics that have allowed the development of such systems.

“Just four years ago, you couldn’t process sophisticated AI through small electronic chips. Now it is completely accessible. In five years, the possibilities will be enormous,” Dr Kavehei said.

The study uses three data sets from Europe and the United States. Using that data, the team has developed a predictive algorithm with sensitivity of up to 81.4 percent and false prediction rate as low as 0.06 an hour.

“While this still leaves some uncertainty, we expect that as our access to seizure data increases, our sensitivity rates will improve,” Dr Kavehei said.

Carol Ireland, chief executive of Epilepsy Action Australia, said: “Living with constant uncertainty significantly contributes to increased anxiety in people with epilepsy and their families, never knowing when the next seizure may occur.

“Even people with well controlled epilepsy have expressed their constant concern, not knowing if or when they will experience a seizure at work, school, traveling or out with friends.

“Any progress toward reliable seizure prediction will significantly impact the quality of life and freedom of choice for people living with epilepsy.”

Dr Kavehei and lead author of the study, Nhan Duy Truong, used deep machine learning and data-mining techniques to develop a dynamic analytical tool that can read a patient’s electroencephalogram, or EEG, data from a wearable cap or other portable device to gather EEG data.

Wearable technology could be attached to an affordable device based on the readily available Raspberry Pi technology that could give a patient a 30-minute warning and percentage likelihood of a seizure.

An alarm would be triggered between 30 and five minutes before a seizure onset, giving patients time to find a safe place, reduce stress or initiate an intervention strategy to prevent or control the seizure.

Dr Kavehei said an advantage of their system is that is unlikely to require regulatory approval, and could easily work with existing implanted systems or medical treatments.

The algorithm that Dr Kavehei and team have developed can generate optimized features for each patient. They do this using what is known as a ‘convolutional neural network’, that is highly attuned to noticing changes in brain activity based on EEG readings.

Other technologies being developed typically require surgical implants or rely on high levels of feature engineering for each patient. Such engineering requires an expert to develop optimized features for each prediction task.

An advantage of Dr Kavehei’s methodology is that the system learns as brain patterns change, requiring minimum feature engineering. This allows for faster and more frequent updates of the information, giving patients maximum benefit from the seizure prediction algorithm.

The next step for the team is to apply the neural networks across much larger data sets of seizure information, improving sensitivity. They are also planning to develop a physical prototype to test the system clinically with partners at the University of Sydney’s Westmead medical campus.

 

via Researchers develop new prediction method for epileptic seizures

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[WEB site] First trial of Cognition Kit wearables demonstrates effectiveness in measuring mental health

The neuroscience company Cambridge Cognition Holdings PLC, which develops near patient technologies for the assessment of brain health, has announced results from a new technology feasibility study. The results demonstrate for the first time that consumer grade wearables such as the Apple Watch® and Microsoft Band can be used to accurately measure clinically relevant cognitive performance in everyday life using the Company’s new Cognition Kit software.

Mental health conditions are among the leading causes of disability worldwide. With more than 450 million people living with mental illnesses, the cost of treatment and care to global economies will double by 2030 to over $6 trillion (Source: World Health Organization).

Current methods of brain health assessment rely on infrequent snapshots to characterise impairment and recovery. Such sparse sampling will often miss clinically significant changes, which can impact on a patient’s quality of life and limit the ability to accurately measure the effect of intervention and treatment.

Cognition Kit is a wearable software platform developed under a joint venture between Cambridge Cognition and London research agency Ctrl Group to address this growing need. The technology will enable doctors, scientists and patients to better understand and manage day-to-day brain health by measuring the key biological and psychological factors affecting mental performance accurately in real time.

The new study shows for the first time that wearable consumer devices can be used clinically to measure cognitive performance accurately when programmed with the Cognition Kit software.

During the study participants wore a wearable device to monitor their levels of stress and physiological activity using built-in sensors of heart rate, galvanic skin response and skin temperature.

Throughout each day, subjects completed game-like micro tests of cognition on the device to measure attention, memory, mood and reaction speed.

After each cognitive game, subjects reported how they felt by selecting one of six faces to convey their current mood. On June 24th, the day of the EU referendum results in the UK, the researchers observed a significant drop in the general mood of the British participants in the study.

The 30 million data points recorded demonstrate distinct patterns of performance within and across days, allowing a rich picture of a subject’s cognitive health to emerge. Cognition Kit thus has the potential to revolutionise brain health treatment at all stages – from patient assessments during the development of disease-modifying interventions to monitoring of patient health.

With drug development companies increasingly being required to demonstrate clinical outcomes-based value of treatments in patients, this Cognition Kit study provides evidence that new technologies could transform healthcare and medical research in a wearable health industry estimated to be worth $2 billion (Source: Soreon Research Wearable Healthcare Report 2014).

Cambridge Cognition is in discussion with a number of pharmaceutical partners following significant early interest boosted by the results of the study and expects to sign the first Cognition Kit contracts in the near future.

Francesca Cormack, PhD, Director of Research and Innovation, Cambridge Cognition commented

”This proof of concept study demonstrates for the first time that these consumer devices are enabling the rapid and accurate collection of largescale scientific datasets. This not only allows dramatically more detailed knowledge of moment-by-moment brain function but also opens up new possibilities to develop machine learning algorithms that will enable earlier detection and intervention in brain disorders.”

Ben Fehnert, Co-founder of Ctrl Group and Director of Cognition Kit commented

”Simple, regular interaction with peoples own phones and wearable devices is key to helping understand daily and longer term fluctuations in cognitive function. This study is the first demonstration of how Cognition Kit software can build a rich picture of brain health using peoples own devices during their daily lives.”

About Cognition Kit

Cognition Kit is a joint venture between Cambridge Cognition and Ctrl Group formed in 2016 to develop digital health tools on mobile and wearable devices. Cognition Kit software takes research out of the lab and into daily life, enabling doctors, scientists and the public to better understand and manage day-to-day brain health.

Source: First trial of Cognition Kit wearables demonstrates effectiveness in measuring mental health

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