SPINDLE SEARCH: The test Mignot’s team designed to determine whether or not humans were better than algorithms at picking out distinct sleep patterns from EEG dataPETER WELINDER/SIMON WARBY; ALMONROTH/WIKIMEDIA COMMONS
Unlike many other physiological measurements that have been relegated to automation, the detection of the more subtle stages of sleep has remained within the purview of experts in sleep clinics. But relying on humans for data processing is tedious, costly, and vulnerable to subjectivity, so researchers have been developing a number of automated methods to single out patterns of brain waves in EEG recordings. Emmanuel Mignot, director of the Stanford Center for Sleep Sciences and Medicine, wanted to see how those automated methods hold up against the detection abilities of both experts and nonexperts viewing the same EEG readings.
Of particular interest to Mignot are sleep spindles, small bursts of activity characteristic of stage-2 sleep, a 20-minute period in which body temperature drops and the ...
