A new study shows that a device that passively monitors breathing during sleep can not only detect Parkinson’s, but also track the progression of the disease over time. The researchers used an artificial intelligence tool to sift through mountains of data from study participants to find patterns that identify the disease and determine severity.
“I like to compare our understanding of Parkinson’s to a street lamp in the night; we only get a glimpse of the disease when patients visit clinic. Moreover, the methods we use to track the disease over time are subjective,” said Ray Dorsey, M.D., a professor of Neurology at the University of Rochester Medical Center (URMC) and co-author of the study. “As a result, we have a very limited insight into how Parkinson's disease impacts people's daily lives. This study shows that remote monitoring has the potential to identify individuals with Parkinson’s and create an objective measure of severity and progression. This could be a powerful tool to detect the disease early and conduct research more efficiently.”
The research, which appears in the journal Nature Medicine, was led by Dina Katabi, Ph.D., a professor of Electrical Engineering and Computer Science at MIT. Katabi worked closely with researchers at the URMC Center for Health + Technology (CHeT), including Dorsey and Chris Tarolli, M.D., an assistant professor of Neurology. This study is one of several projects supported by CHeT that are exploring new ways to harness remote monitoring, smart phones, smart watches, and other technologies to improve care and advance research in Parkinson’s and other diseases. The study also included researchers from the Mayo Clinic, Massachusetts General Hospital, and Boston University.
Parkinson’s is the fastest-growing neurological disorder in the world, outpacing even Alzheimer’s, and more than one million Americans are currently living with the disease. While there are rare genetic forms of the disease, many case of Parkinson’s are likely caused by exposure to certain industrial chemicals and pesticides.
Dr. James Parkinson noted changes in breathing patterns when he first described the disease in the early 19th century. The new research takes inspiration from this 200-year-old observation and targets this symptom of the disease in an effort to see if nocturnal breathing rhythms, and changes to these rhythms over time, can be analyzed to create a digital biomarker of Parkinson’s. The study employed a device that passively emits radio signals that capture breathing patterns, the pulsing of blood vessels, and muscle movement during sleep.
The researchers recruited 7,687 participants, including 757 individuals with Parkinson’s, and recorded 120,000 hours of sleep. The data was then analyzed by MIT researchers using a form of AI called a neural network, a series of connected algorithms that sort through vast qualities of data in search of patterns. The model was able to differentiate between volunteers with Parkinson’s and those without.
There are currently no effective bio-markers to diagnose Parkinson’s, particularly in the early stages, and track its progression. Often by the time motor symptoms of the disease first emerge and a diagnosis is made, a large percentage of the dopamine-producing neurons targeted by the disease have already died off. An early diagnosis could enable patients to start treatments earlier, potentially forestalling the progress of the disease. More precise measurement of the progression of the disease – which can vary greatly from patient to patient – will also enable scientists to better measure if experimental therapies are working. Proven remote monitoring technologies will also allow researchers to recruit study participants more widely, measure the impact new therapies more quickly and, hopefully, find new effective treatments faster.