Clinical sleep analysis has historically centered on identifying and
tracking common patterns of brain and physiological activity, called
sleep stages. Identifying sleep stages has long been a time-consuming
and subjective process.
Starting in the late 1930s, sleep staging was
performed using electroencephalogram (EEG) machines that would cut a paper tape into sheets
with 30-second traces of the patient's brainwave activity. A skilled
technician would painstakingly take each paper sheet - almost 1,000 in
an eight-hour sleep recording - and decide which sleep stage the patient was
in by visual inspection of the EEG traces.
‘By applying a technique called multitaper spectral analysis to electroencephalogram (EEG) data provides objective, high-resolution depictions of brainwave activity during sleep that are more informative and easier to characterize than previous approaches.’
Almost 80 years later, other than slight refinement of the stages
and the fact that the 30-second EEG traces now appear on a computer
screen, the process of sleep staging remains virtually unchanged,
remaining a time-consuming and fundamentally qualitative process.
Consequently, even experienced scoring technicians still agree only 75
to 80% of the time.
Massachusetts General Hospital (MGH) investigators have developed a
novel approach to analyze brainwaves during sleep, which promises to
give a more detailed and accurate depiction of neurophysiological
changes than provided by a traditional sleep study.
In a report
published in the January issue of Physiology
, the research team
describes how applying a technique called multitaper spectral analysis
to electroencephalogram (EEG) data provides objective, high-resolution
depictions of brainwave activity during sleep that are more informative
and easier to characterize than previous approaches.
also present a visual atlas of brain activity during sleep in healthy
individuals, highlighting new features of the sleep EEG - including a
predictor of REM sleep - that could be of important use to clinicians
The progression of sleep stages over a night,
called a hypnogram, is still used as the primary descriptor of sleep
architecture. While the hypnogram has been an important tool for
describing sleep architecture, since the numerous bumps and squiggles of
brainwave traces become undiscernible by eye over large time scales,
there are important drawbacks to relying on subjective summaries of
sleep instead of objective data.
"During sleep, the brain is engaged in a symphony of activity
involving the dynamic interplay of different cortical and sub-cortical
networks," says Michael Prerau of the MGH Department of Anesthesia, Critical Care and Pain Management, lead author of the Physiology
report. "Due to practical constraints and established practices,
current clinical techniques greatly simplify the way the sleep is
described, causing massive amounts of information to be lost. We
therefore wanted to identify a more comprehensive way of characterizing
brain activity during sleep that was easy to understand and quick to
learn, yet mathematically principled and robust."
The approach used by the MGH investigators provides a paradigm shift
allowing clinicians to move away from subjective sleep staging and
harness the wealth of objective information contained within EEG data.
In their report, the team describes how sleep oscillations are far more
easily characterized using spectral estimation than by looking at EEG
traces. Spectral analysis is a class of approaches that break a waveform
signal into its component oscillations - repeating patterns over time-
just as a prism breaks white light into its component colors. In the
EEG, these oscillations represent the activity of specific brain
networks during sleep and wakefulness.
"At a fundamental level, brain activity is truly organized in terms
of oscillations and waves," says senior author Patrick Purdon, MGH
Anesthesia. "Spectral analysis is just analyzing the signals in terms of
these waves, making it the right tool - and in some ways the perfect
tool - for the job." Purdon also points out that traditional sleep
scoring is essentially a crude form of spectral analysis, based on
recognizing the wave properties by eye.
Spectral analysis may not have been adopted for sleep scoring
previously because the prevailing techniques for EEG spectral estimation
produced noisy and inaccurate estimates of the power spectrum, making
interpretation of the resulting spectrogram difficult. Consequently, the
MGH team employed multitaper spectral analysis, a form with greatly
reduced noise and increased accuracy compared to more basic methods.
Computing a multitaper spectrogram of the sleep EEG, which tracks how
the power and frequency of these oscillations change over time, provides
more information about which networks are active at different points
In their report the researchers use these new vivid images of brain
activity to illustrate how the sleep EEG multitaper spectrogram
objectively reveals the detailed architecture of an entire night of
sleep in a single visualization, rather than 1,000 30-second windows.
Repeating patterns of activity - which use color to reflect signal power
- become apparent even to the untrained eye, allowing technicians with
only a few hours of training to stage with an accuracy comparable to
that of traditional sleep scoring.
The investigators comprehensively detailed an atlas of common
patterns and transitions seen within healthy individuals during sleep
and highlighted the ability of the sleep EEG multitaper spectrogram to
show features on time scales ranging from many hours to microevents
lasting a few seconds. They were also able to identify novel features of
the sleep EEG, including a trend in which bursting in the low-frequency
alpha range, which is not currently used in clinical sleep scoring,
predicts the onset of REM sleep by several minutes. Purdon says, "We try
to remind people that the sleep EEG isn't just a pile of 'big data.' In
fact, it's highly structured, and that structure is deeply connected to
the fundamental brain mechanisms of sleep."
Future research by the team will focus on developing robust
quantitative metrics based on the spectrogram. "Moving forward, this
enhanced approach will allow scientists to better characterize the
complex heterogeneity observed in normal sleep and ultimately to assist
in diagnosing sleep and related disorders," says Prerau, who has
received a grant from the National Institute of Neurological Disease and
Stroke to identify new disease biomarkers within sleep. "It is also fun
and easy to learn!" Prerau has created the website http://sleepEEG.org,
which hosts free, interactive tutorials designed to teach clinicians
and investigators how to read the sleep multitaper spectrogram.
The researchers also are optimistic about the clinical applications
of this method. Co-author Matt Bianchi of the MGH Department
of Neurology and director of the MGH Sleep Lab, says, "The traditional
hypnogram has not had the clinical application one might expect for such
a fundamental aspect of sleep. This technique is poised to bring EEG
patterns, a classic aspect of sleep medicine, back to the forefront.
Overall, by improving physician review of patient data, these techniques
hold promise to bring modern analytics to routine care - making the
patterns of brain activity during sleep accessible and enabling
physicians to see the EEG through this dynamic lens."