Six EEG electrodes // Two electrooculogram leads // Seven electromyogram leads // Two ECG leads // One microphone // One pneumotachograph // Two piezoelectric bands // One oxygen saturation meter // All of which tell only part of the story.
When Robert Thomas, a sleep researcher, approached Ary Goldberger, a cardiologist, Thomas was looking for a way to analyze sleep patterns that didn’t require patients to spend an entire night in a hospital or clinic for a traditional sleep study. Thomas thought that while most sleep research focused on the brain, signals from other parts of the body might also yield crucial information. “Sleep is a brain function, but it’s also a full-body state,” says Thomas, who wanted to see what could be learned by looking at information from the heart.
As it happened, Goldberger’s lab at Beth Israel Deaconess Medical Center in Boston, which specializes in extracting hidden data from complex signals, had already developed a computer algorithm for analyzing sleep patterns using an electrocardiogram, or ECG. (Goldberger had wanted to help fellow cardiologists employ a familiar instrument to diagnose sleep apnea, a disorder that has been found to significantly increase heart disease and mortality. He also thought patients would prefer wearing a heart monitor at home to participating in an overnight sleep study in the clinic, and using ECGs meant doctors could follow patients over several nights to monitor the effects of treatments.) But when the researchers compared ECG-analyzed sleep studies with results from the conventional approach, the two didn’t seem to match up, so they abandoned the effort.
That mismatch didn’t bother Thomas (also at Beth Israel), who was hunting for something different: a heart-generated signal that might correspond to the cyclic alternating pattern, or CAP, a somewhat controversial brain-based indicator of restless sleep. CAP episodes may signal microarousals: frequent, fleeting awakenings that seem to underlie many sleep problems. But CAP patterns are extremely difficult to measure, and a technician who hasn’t been specially trained won’t detect them on an electroencephalogram (EEG), the traditional sleep study’s primary tool.
When Goldberger and his team dusted off their algorithm and compared ECG analyses of sleep studies with those of CAP from EEGs, it was almost a perfect fit. “The CAP patterns just fell out of the data,” Goldberger recalls about seeing ECG patterns that showed up just when CAP episodes occurred. “It was miraculous.”
That may seem like a small advance, and its validity remains to be confirmed. Yet as one of several recent efforts to improve on long-standing ways of gauging sleep disorders, the effort could be significant. Chronic sleep problems rank among the top complaints patients bring to doctors, affecting as many as 70 million Americans, according to the Institute of Medicine. Breathing disorders, including sleep apnea, are among the most serious, yet as many as nine of 10 sleep breathing concerns remain undiagnosed and untreated.
Though one problem may be that some of these patients aren’t seen by doctors, another problem may be how sleep is measured. In 1968 the American Academy of Sleep Medicine (AASM) pooled observations about EEG patterns in the normal sleep of young adults and settled on a system of several distinct sleep stages. During the nearly four decades since then, that early consensus about what happens during sleep has proven to be extraordinarily useful, and it remains the gold standard for understanding sleep problems. Yet it seems increasingly likely that it tells only part of the story of what happens when the head hits the pillow.