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 groups where other commonly used methods fail. The novel approach is tested on accelerometer data gathered from an ongoing study at the University of Michigan. While most standard approaches to signal processing show no difference between the two groups, windowed persistent homology and the modified Hausdorff semimetric show a clear separation. This has significant implications for clinical decision making and patient care.
Highlights
As medical monitoring devices continue to grow in complexity and shrink in size, both the number of possible concurrent measurements and the size of the observable population increase
Entropy as a measure of disorder and information can often distinguish between functional classes of data, in particular when dealing with biomedical signals, and is heavily used in the signal processing literature
To predict a priori weight loss/maintenance success in overweight or obese individuals by applying information learned from one week of simple, noninvasive measures would heavily impact the healthcare industry
Summary
As medical monitoring devices continue to grow in complexity and shrink in size, both the number of possible concurrent measurements and the size of the observable population increase. These factors in turn result in a rise in the amount of data available for analysis, which is driving the need for new processing algorithms. The need for novel signal processing algorithms that can detect and highlight. We have filed an invention disclosure with the University of Michigan. This does not alter our adherence to PLOS ONE policies on sharing data and materials
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