New tech could diagnose diseases like Parkinson’s by interpreting how quickly and often you blink

Researchers have developed a device that has the potential to diagnose neurological and autoimmune diseases by monitoring and interpreting eyelid movements.

The device, designed by researchers at the Technion-Israel Institute of Technology, attaches to glasses, and was first used to diagnose the disease blepharospasm dystonia, which is characterised by involuntary contraction of the muscles responsible for closing the eyes.

In using the device to spot blepharospasm dystonia, the researchers found a statistically significant quantitative relationship between a person’s eyelid pattern and the disease.

The tech was then used to examine the effect of Botox injections, the conventional treatment for the disease, and it was found that within 15 minutes contractions decrease and patients’ blinking rates begin to match the patterns associated with healthy people.

Image courtesy of The Technion-Israel Institute of Technology

“Eyelid motion provides us with meaningful information about the health of a patient,” said Adi Hanuka, a doctoral student at the Technion-Israel Institute of Technology.

“This motion can indicate not only eye diseases, but also neurological diseases such as Parkinson’s, and autoimmune diseases such as Grave’s. We developed a device that can be installed on the standard refraction glasses used in eye tests.”

According to the researchers, the device has the potential to diagnose every disease that is expressed neurologically, including many ocular and systemic such as Ptosis, Thyroid eye disease, Parkinson’s disease, Myasthenia Gravis, and neurologic diseases such as third and seventh cranial nerve palsy.

Lead researcher Adi Hanuka. Image courtesy of The Technion-Israel Institute of Technology

“Along with designing the product for purposes of commercialisation, we are working in several directions: developing the device as a platform for multidisciplinary research on various topics such as the effect of emotions on blinking patterns; eyelid communication amongst the paralyzed; and automatic diagnosis through machine learning and based on a computerized comparison between the specific monitoring and an extensive database,” said Hanuka.

In order to define the eyelid motion patterns, which includes blinking speed and frequency, of healthy people, measurements of approximately 100 subjects were collected.Eyelid motions were then analysed using a specifically tailored signal-processing algorithm.

Over the past two years, the device has been used in clinical trials at Haemek Medical Center in Afula, Israel.

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