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Detecting Parkinson’s disease with home sensors

Detecting Parkinson’s disease with home sensors
Detecting Parkinson’s disease with home sensors

 


A new study shows that a device that passively monitors breathing while you sleep can not only detect Parkinson’s disease, but track the disease’s progression over time. , sifted through vast amounts of data from study participants to find patterns that identified and determined disease severity.

“I like to liken our understanding of Parkinson’s disease to street lights at night. The methods used are subjective,” said Ray Dorsey, MD, professor of neurology at the University of Rochester Medical Center (URMC) and co-author of the study. “As a result, we have very limited insight into how Parkinson’s disease affects people’s daily lives. We demonstrate the potential to create objective measures of severity and progression, which could be a powerful tool for early detection and more efficient research of disease. ”

Research published in journals natural medicineled by Dr. Dina Katabi, Professor of Electrical Engineering and Computer Science at MIT.Katabi worked closely with his URMC researchers Center for Health + Technology (CHET), including Dorsey and Chris Tarori, MD, Assistant Professor of Neurology. The study is supported by several CHeT-supported researchers seeking new ways to use remote monitoring, smartphones, smartwatches and other technologies to improve care and advance research in Parkinson’s disease and other conditions. One of the projects. The study also included researchers from the Mayo Clinic, Massachusetts General Hospital, and Boston University.

Parkinson’s disease is the world’s fastest-growing neurological disorder, surpassing Alzheimer’s disease, with more than one million Americans currently living with it. Although the disease has rare genetic forms, many cases of Parkinson’s disease can be caused by exposure to certain industrial chemicals and pesticides.

When Dr. James Parkinson first described the disease at the age of 19, he noted changes in breathing patterns.th century. Inspired by his 200-year-old observations, a new study looks at symptoms of the disease, analyzes nighttime breathing rhythms, and changes in these rhythms over time, to explore Parkinson’s disease. I’m trying to see if I can create a digital biomarker for him. The study used a device that passively emits wireless signals that capture breathing patterns, vascular pulsations, and muscle movements during sleep.

Researchers recruited 7,687 participants, including 757 with Parkinson’s disease, who recorded 120,000 hours of sleep. The data was then analyzed by his MIT researchers using a form of AI called neural networks. It is a series of connected algorithms that search for patterns and classify data of enormous quality. The model was able to distinguish between volunteers with Parkinson’s disease and those without.

Currently, there are no effective biomarkers to diagnose Parkinson’s disease, especially in its early stages, and to track its progression. In many cases, by the time motor symptoms of the disease first appear and the diagnosis is made, most of the dopaminergic neurons targeted by the disease are already dead. Early diagnosis allows patients to start treatment earlier and prevent disease progression. By more accurately measuring disease progression, which can vary greatly from patient to patient, scientists will be able to measure more accurately whether treatments are working. Proven telemonitoring techniques will enable researchers to recruit study participants more widely, measure the impact of new treatments more quickly, and hopefully discover new effective treatments more quickly.

Sources

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2/ https://www.urmc.rochester.edu/news/story/at-home-sensors-can-spot-parkinsons-disease

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