填空题 Scientists have found a cheap and easy way of {{U}} {{U}} 1 {{/U}} {{/U}}a condition from recordings of people sleeping. Severe snoring is the sound of a sleeper fighting for {{U}} {{U}} 2 {{/U}} {{/U}}. Lots of people snore, but the loud and {{U}} {{U}} 3 {{/U}} {{/U}}snoring caused by a condition known as {{U}} {{U}} 4 {{/U}} {{/U}}sleep apnea, OSA, can leave a sufferer {{U}} {{U}} 5 {{/U}} {{/U}}and fuddled during the day.
OSA is costly and {{U}} {{U}} 6 {{/U}} {{/U}}to diagnose, and it's difficult to distinguish genuine OSA from {{U}} {{U}} 7 {{/U}} {{/U}}snoring. But a team in Brazil has a simpler solution: they have found a way of analyzing snore recordings that is able not only to {{U}} {{U}} 8 {{/U}} {{/U}}OSA but can distinguish between mild and {{U}} {{U}} 9 {{/U}} {{/U}}cases.
Diagnosing OSA from snore sounds is not a new idea. The question is how the clinical condition is revealed by the {{U}} {{U}} 10 {{/U}} {{/U}}. In 2008, a team in Turkey showed that the statistical {{U}} {{U}} 11 {{/U}} {{/U}}of snores has the {{U}} {{U}} 12 {{/U}} {{/U}}to discriminate ordinary sleepers from OSA {{U}} {{U}} 13 {{/U}} {{/U}}.
Scientists looked for {{U}} {{U}} 14 {{/U}} {{/U}}patterns in OSA and the snore {{U}} {{U}} 15 {{/U}} {{/U}}can be used as a pretty reliable {{U}} {{U}} 16 {{/U}} {{/U}}for the AHI (the apnea-hypopnea index). And "snore {{U}} {{U}} 17 {{/U}} {{/U}}" is measured by a Hurst exponent, which reveals {{U}} {{U}} 18 {{/U}} {{/U}}patterns in a series of events. An {{U}} {{U}} 19 {{/U}} {{/U}}computer analysis of the snore series could "learn" to use the Hurst exponent to distinguish {{U}} {{U}} 19 {{/U}} {{/U}}from severe cases of OSA, making the correct diagnosis for 16 of 17 patients.