Heart Tracings Reveal Sleep Patterns for Health and Disease

Stable and Unstable Sleep Found Distinct from Traditional Non-REM Stages

During the most restful sleep, the resting heart speeds up and slows down slightly with each breath in and out. But when the heart rhythm drops out of sync with breath-to-breath respiration, slumber becomes more fitful and tiring.

Photo by Graham Ramsay

An electrocardiogram could provide more information on sleep stability and quality than a dozen monitoring devices, say (clockwise from top left) Chung-Kang Peng, Robert Thomas, Joseph Mietus, and Ary Goldberger.

Efforts Wake Science to Sleep Classification Almost 40 years ago, a dozen sleep scientists grew concerned about the reliability of the way they scored stages of sleep in their research. One weekend in California, they met to refine a set of standards for classifying the cycle of sleep as wakefulness, rapid eye movement (REM), and non-REM. A major sticking point came in discussions about how to further classify the obvious variations in non-REM sleep. At one point during the extended debate, Allan Rechtschaffen from the University of Chicago, who co-authored the resulting manual, locked the door and said he would not let them leave until they could come to a consensus. It was an empty threat, but it worked. They divided non-REM sleep into four stages known as arousal thresholds, which roughly correlate with the amount of shouting and shaking required to wake someone up. The manual was published in 1968, providing an important tool for modern sleep research. The rapidly expanding field of sleep medicine also relied on the manual, even though the supporting data was mostly based on observations of healthy people. “It gave people a language and a tool with which they could compare observations,” said Wolfgang Schmidt-Novarro at the Sleep Medicine Institute at Presbyterian Hospital of Dallas, “but it doesn’t do a particularly good job of providing insights or parameters for diagnosing disease.” For the first time, the manual is being revised to include more clinically relevant standards, such as the addition of respiratory and cardiac criteria. Extensive evidence from people with sleep disorders will underlie some of the changes. Where sufficient evidence does not exist, the reviewers will revert to consensus. The staging of non-REM sleep has remained problematic. The reworked manual is not due to be finished until summer 2006, but it appears that scoring of non-REM sleep may be compressed into three stages, said Conrad Iber of the University of Minnesota, who is heading the process for the American Academy of Sleep Medicine. Digital analyses, such as the new study by Robert Thomas and his colleagues, will be considered, but the studies are probably too preliminary to be included in the new manual, Iber said. “It’s missed that boat,” said Thomas. “Maybe in the next reclassification. ” The new standards will undergo review every five years, said Iber. In the meantime, Thomas has integrated the new knowledge into his clinical practice by more precisely tuning treatment to enhance stable sleep. And he and his colleagues have launched new studies to evaluate clinical correlates of stable and unstable sleep.

Normally, a person must be wired and monitored from head to toe to assess sleep quality. An overnight sleep evaluation continuously tracks brain waves, eye movements, snoring, leg twitches, teeth grinding, and much more. Now, just one of the many tracings, a simple continuous electrocardiogram (ECG), may be able to do the job of a suite of independent instruments, researchers at Beth Israel Deaconess Medical Center report in the September Sleep.

“This is a distal but clean biomarker that tells us if the system is oscillating in synchrony with each breath or over multiple breaths,” said first author Robert Thomas, head of the BID sleep laboratory and HMS instructor in medicine. “This reflects stable and unstable sleep behavior. Disease expands the unstable behavior of the system. The goal of treatment is to enhance the stable behavior.”

If validated by further studies, the ECG as a measure of sleep stability may be an easier and less expensive way of diagnosing and guiding the therapy of sleep disorders.

The work also lends credence to a nonconventional way of thinking about the stages that make up most of a good night’s sleep, known as non–rapid-eye-movement (non-REM) sleep. The researchers’ ECG analysis revealed two states of non-REM sleep, stable and unstable. In contrast, the traditional staging system divides non-REM sleep into four grades ranging from light to deep sleep, which correlate with the effort needed to wake someone up. The sleep staging standards are now being reevaluated (see sidebar).

Heart of Sleep
In overnight evaluations, multiple lines of data spike and plunge across a large computer screen in real time as the seconds and minutes go by. When one measurement changes—reflecting a gasp, a snore, or a shift in body position—most of the other markers change in synchrony. “A sleep study is like an orchestral score of the music of the sleeping body,” Thomas said.

“The breathing and heart rate control turn out to have a profound connection to what’s going on in the brain during sleep,” said senior author Ary Goldberger, director of the Margret and H.A. Rey Institute for Nonlinear Dynamics in Medicine at BID and an HMS professor of medicine. “It ratifies the growing consensus of the importance of cross talk and systems biology. You have one set of conversations going on between the heart and lungs and nervous system in health. In pathology, the frequency and tone of that conversation literally changes and you see a new conversation emerge.”

Thomas began to wonder about non-REM sleep about five years ago. He was becoming both intrigued and frustrated by spontaneous flips back and forth between stable and unstable patterns of sleep in patient after patient. Bad sleep could suddenly change to good and vice versa without any intervention and while remaining in the same grade of non-REM sleep.

Another Look at Non-REM
Brain waves are the gold standard in sleep medicine. Thomas noticed that the electroencephalogram patterns of these Jekyll-and-Hyde sleep patterns had been described by a group of Italian researchers as cyclic alternating patterns (CAP) and non-cyclic alternating patterns (non-CAP). In several published studies, Thomas correlated the CAP/non-CAP to measurements of sleep quality and sleep disorders. Seeking an independent validation, he turned to Goldberger’s group.

Meanwhile, co-author Joseph Mietus, a BID bioengineer, had shelved an initially disappointing algorithm he had devised to deconstruct a single jagged ECG plot to show the link between the heart rate and breathing dynamics. He had hoped it would help diagnose sleep apnea, but he could not correlate his results with the classic non-REM sleep staging standards. Colleague and co-author Chung-Kang “C.K.” Peng, a statistical physicist and co-director of the Rey lab, thought the approach had promise and urged him to pursue it further.

“You have one set of conversations going on between the heart and lungs and nervous system in health. In pathology, the frequency and tone of that conversation literally changes and you see a new conversation emerge.”

When Thomas first asked them for a mathematical way to distinguish between CAP and non-CAP sleep in his sleep study datasets, Goldberger, Mietus, and Peng were daunted by what they envisioned as an insolvable problem.

But because Goldberger directs PhysioNet, a research resource funded by the National Institutes of Health, his group felt a responsibility to assist Thomas, an NIH-funded researcher. So Mietus ran a sample dataset through his algorithm, expecting it would probably be a dead end.

“It turned out to be enormously exciting,” Goldberger said. They refined the technique with 70 sleep studies on patients from BID and other accredited sleep centers. The stable and unstable sleep patterns overlapped, but were not identical to, the CAP/non-CAP brain wave patterns. When they retested the trained algorithm on data from 15 healthy people who were part of a different study, they discovered that stable and non-stable sleep were a feature of normal sleep in healthy individuals.

The landscape of sleep. The spectrogram above, created by Joseph Mietus, demonstrates the difference between stable and unstable sleep. This nocturnal electrocardiogram of a healthy man, 24, shows normal fluctuations between stable (top range) and unstable (bottom range) sleep throughout the night. The top mountain range nearly disappears in people with uncorrected sleep disorders. Stable and unstable sleep patterns appear to be independent of classic sleep staging for the same person, shown in the upper bar: wake, rapid eye movement (REM), and non-REM stages 1 through 4.

(Courtesy Joseph Mietus)

Mietus devised a quick way to visualize the results using a “sleep spectrogram” with two distinct mountain-range bands. Healthy people show more stable sleep; people with untreated sleep disorders show more unstable sleep. The stable and unstable sleep patterns do not correlate with conventional non-REM sleep staging, suggesting a complementary new view of sleep regulation and physiology.

“We are not proposing a new sleep classification system,” Thomas said. “We’re saying this is how non-REM sleep works. The field can decide what to do with the new information and how to use this new tool.”

—Carol Cruzan Morton

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