What Do Your Eyes Say? Device Can Diagnose Diseases Based on Eyelid Motion
October 2, 2017
Researchers at the Technion’s Andrew and Erna Viterbi Faculty of Electrical Engineering have developed a device that can diagnose diseases by means of an eyelid motion monitor (EMM). The project was published recently in Graefe’s Archive for Clinical and Experimental Ophthalmology.
Already in its developmental stages, the device has won several international awards, and was ranked in the top 20 in the Texas Instruments Innovation Challenge (TIIC) – Europe Design Contest. Over the past two years, the device has been used in clinical trials at Haemek Medical Center in Afula, Israel.
The device was first developed by Technion Prof. Levi Schachter and doctoral student Adi Hanuka, who began working on it as an undergraduate. Hanuka continued working on it during her graduate studies, with the help of her students Alon Berger and Maor Itzhak (as part of their final undergraduate project).
“Eyelid motion provides us with meaningful information about the health of a patient,” explained Hanuka. “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, at the request of Dr. Daniel Briscoe, Chairman of the Haemek Medical Center Department of Ophthalmology.”
Glasses are fitted with a hardware and software system that monitors and interprets eyelid movements. With approval of the Ethics Committee Regulations for Research Work Involving Human Participants, measurements of approximately 100 subjects have been collected in order to define the eyelid motion patterns (blinking speed and frequency) of a healthy person. Eyelid motions were analyzed using a signal-processing algorithm written by students Tal Berkowitz, Michal Spector, Shir Laufer, and Naama Pearl.
The team first examined blepharospasm dystonia, a disease characterized by involuntary contraction of the muscles responsible for closing the eyes. The researchers found a statistically significant quantitative relationship between a person’s eyelid pattern and the disease, which means that the device could be used to diagnose it. The system was also 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 the blinking pattern begins to match indices that exist among healthy people.
The researchers are also gathering information about other groups, including patients with dementia and Parkinson’s disease.
“Along with designing the product for purposes of commercialization, 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.
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