Researchers from two Michigan universities have made a collaborative effort to improve sleep analysis methods by means of computer algorithms, quantitative analysis, and of course, the latest in technology. One of the new approaches was found to help weed out fibromyalgia patients from healthy subjects.
Chief among the researchers in this science talent roundup are Joseph W. Burns, a research scientist and engineer at the Michigan Tech Research Institute (MTRI); Ronald D. Chervin, director of the University of Michigan’s Michael S. Aldrich Sleep Disorders Laboratory; and Leslie Crofford, director of the Center for the Advancement of Women’s Health at the University of Kentucky. Each of these researchers brings specific resources to the table for the purpose of furthering our understanding of sleep.
The University of Michigan’s sleep laboratory is an important cog in the wheel of sleep medicine and MTRI has contributed its expertise by way of specially designed remote sensors that are capable of collecting and analyzing data by way of signal processors that use algorithms or computer programs. This collaboration between the University of Michigan and MTRI enabled MTRI to begin to apply quantitative analysis, computer algorithms, and remote sensor technology to actual clinical cases.
This fine tuned methodology has turned up data that has significant clinical relevance. The newest data relating to sleep fragmentation seem to correlate to the levels of pain experienced by fibromyalgia sufferers.
Chervin explained that patients with sleep disorders tend to be subjected to costly and time-consuming tests in sleep laboratories. Patients undergoing such testing in sleep labs have needed to be monitored for brain, heart, and muscle activity during the course of a night. This data then had to be assessed by specially trained technicians the old fashioned manual way. “We are collaborating to find new ways to analyze routinely collected data in a way that will be meaningful to the patient’s health and will help us understand how sleep disorders affect brain functions,” he said.
If data analysis can be automated, the cost of such analysis will be reduced with the possible added benefit of greater accuracy in the interpretation of results. MTRI and the U of M have already found a way to automate a technique that can assess the extent of the type of breathing associated with sleep disorders through only two signals: respiration and brain waves. Until now, 12 or more signals were needed for a manual scoring of disordered sleep breathing.
Burns suggested that, “It may even become possible for people to take sleep tests—simpler and more effective than some of those currently available—at home where they can sleep in their own familiar bedrooms.