Integrated Sleep Stability:
Dynamic Mapping of Sleep Oscillations in Health and Disease

For a long time, researchers have used electroencephalogram (EEG) data to measure sleep on a discrete scale.  The traditional technique is to determine whether a person is experiencing REM (rapid eye motion) sleep and to then fit non-REM sleep into one of various stages.  Although this method works well for research purposes, it doesn’t work very well in clinical practice because the vast majority of natural sleep is classified as non-REM stage 2.  The current classification system is also imperfect because it does not capture the effects that sleep produces outside the brain.  In the lab of Robert Thomas, researchers are attempting to address this problem by developing a new sleep-measurement rubric that incorporates heart and respiration rate information. 

Their method is based on the fact that many signals from the sleeping body oscillate in a coupled fashion.  This coupling can be grouped into three categories: high-frequency coupling (HFC), low-frequency coupling (LFC), and very-low-frequency coupling (VLFC).  The frequency of HFC is the same as that of respiration.  Over the course of a night, the amount of each type of coupling can be measured, and this information can be depicted on a sleep spectrum.  Dr. Thomas’s team produced sleep spectra for a number of people with sleep apnea, and they found that continuous positive airway pressure (CPAP), a treatment for sleep apnea, caused these people to experience increased HFC.  They also analyzed data from patients with heart failure, and they found that these subjects experienced a tremendous amount of LFC but very little HFC.  Thus, it seems that HFC can be used as a biomarker for healthy sleep.  The sleep spectra produced by Dr. Thomas’s lab seem to be “sleep thermometers” that might someday allow doctors to gain insight into many aspects of a patient’s physiology.  This approach challenges the current understanding of sleep because it incorporates more than brain waves and because it breaks from the discrete stages that are currently used to describe sleep.  

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