"At a fundamental level, brain activity is truly organized in terms of oscillations and waves," says senior author Patrick Purdon, PhD, MGH Anesthesia. In the EEG, these oscillations represent the activity of specific brain networks during sleep and wakefulness. 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 their report, the team describes how sleep oscillations are far more easily characterized using spectral estimation than by looking at EEG traces. 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. 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." "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. "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, PhD, of the MGH Department of Anesthesia, Critical Care and Pain Management, lead author of the Physiology report. 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. The progression of sleep stages over a night, called a hypnogram, is still used as the primary descriptor of sleep architecture. Consequently, even experienced scoring technicians still agree only 75 to 80 percent of the time. A skilled technician would painstakingly take each paper sheet - almost 1,000 in an 8-hour sleep recording - and decide which sleep stage the patient was in by visual inspection of the EEG traces.Īlmost 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. Starting in the late 1930s, sleep staging was performed using EEG machines that would cut a paper tape into sheets with 30-second traces of the patient's brainwave activity. Identifying sleep stages has long been a time-consuming and subjective process. The researchers 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 and researchers.Ĭlinical sleep analysis has historically centered on identifying and tracking common patterns of brain and physiological activity, called sleep stages. 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. ![]() 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. ![]() Characteristic patterns clearly differentiate waking (left) from nonREM (center) and REM sleep (left). Image: The sleep EEG multitaper spectrogram reveals patterns of continuous changes in brain oscillation activity during sleep.
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