Studying your sleep
Tech research group, U of M taking innovative stepsBy GARRETT NEESE, DMG Writer
HOUGHTON - You may be able to distinguish fibromyalgia sufferers from people without the ailment by looking at how they sleep.
The finding was the latest result of the ongoing collaboration between research engineers from Michigan Technological University and sleep specialists from the University of Michigan.
The collaboration between the two began around 2002, said Joseph Burns, a research scientist and engineer at the Michigan Tech Research Institute (a freestanding research institute which Tech purchased in 2006). A co-director of the institute had a meeting with Ronald Chervin, director of the University of Michigan's Michael S. Aldrich Sleep Disorders Laboratory.
Chervin was interested in finding different methods of assessing sleep, as the existing models weren't accurate enough in predicting neurobehavioral and health outcomes. He also wanted to look at ways of automating the process of analyzing signals. The Tech Research Institute has worked in analyzing electric signals in projects involving remote sensing technology.
"From an electrical engineering perspective, it's a signal, it's a period of time ... if you have multiple signals, you can look at the relationships between signals to tell you what's going on," Burns said.
Serious studies in the field of sleep medicine began 20 years ago. Typically, Burns said, the patient will come down to a hospital bedroom to sleep while they're attached to sensors measuring a variety of factors such as heart rate, breathing and muscle activity.
"The traditional method of analyzing it out is just collect it, and record it, and visually inspect it," he said.
Fibromyalgia, a chronic muscle and ligament condition, is also commonly linked with fatigue. A person with normal sleeping habits will go through two or three big cycles, including deep, restorative and light cycles. With fibromyalgia, that gets disrupted.
MTRI analyzed the average amount of time in each sleep stage to develop a metric to find when people had more sleep fragmentation. A higher score on the index correlated with fibromyalgia.
"Even though people had for quite a while through talking to (fibromyalgia) patients understood their sleep was disrupted, people hadn't come up with a really good metric of taking the study, finding an algorithm and finding a correlation between them," Burns said.
Burns and Chervin, along with Leslie Crofford, director of the Center for the Advancement of Women's Health at the University of Kentucky, reported the results of the study in the current issue of the journal Sleep Medicine.
Burns' team is working with Chervin on using signal-processing technology to record and analyze brain waves and biophysical responses of people of various ages and sleep disorders.
Previous testing standards have measured as many as 16 different signals; Chervin's scaled down model investigated the relationship between EEG (electroencephalography), which measures the brain's electrical activity and respiration.
The standard measure of sleep disorders is to mark when patients stop breathing, Burns said; that number is divided by time, to come up with an apnea-hypopnea index. That correlates well, but not perfectly, with some outcomes.
By using an automated technique, Burns said, you can study sleep disruptions that occur before breathing is interrupted.
"Look at how the EEG changes in concert with the breathing," he said. "You might be able to come up with a measure ... if you're having struggles with respiratory problems, you brain might struggle on every single breath, or having sleep fragmentation throughout the night," he said.
A link was also found between sleep disorders and attention deficit hyperactivity disorder, suggesting that some behavior diagnoses may stem from sleep fragmentation, Burns said.
While it wasn't a primary goal of the research, Burns said the two-variable test, with its reduced number of sensors, might result in cheaper tests that could be administered at home.
"If you only have a couple channels instead of 16, it's much more practical for a take-home test," he said.
Other sleep disorder research is focusing on developing an automated assessment for REM (rapid-eye movement) disorder, where people physically act out the action in their dreams, a possible link between sleep fragmentation and bullying, as well as examining how sleep disorders in breathing affects children with asthma.
"We started initially on one problem and now there's a wide range of things," he said. "By analyzing this data, we're trying to branch out into different related biomedical things."
Garrett Neese can be reached at gneese@mininggazette.com
