Windowed persistent homology: A topological signal processing algorithm applied to clinical obesity data.

ABSTRACT: Overweight and obesity are highly prevalent in the population of the United States, affecting roughly 2/3 of Americans. These diseases, along with their associated conditions, are a major burden on the healthcare industry in terms of both dollars spent and effort expended. Volitional weight loss is attempted by many, but weight regain is common. The ability to predict which patients will lose weight and successfully maintain the loss versus those prone to regain weight would help ease this burden by allowing clinicians the ability to skip treatments likely to be ineffective. In this paper we introduce a new windowed approach to the persistent homology signal processing algorithm that, when paired with a modified, semimetric version of the Hausdorff distance, can differentiate the two group

SUBMITTER: Biwer C 

PROVIDER: S-EPMC5428980 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature