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The Utility of Shapelets for Analyzing Physical Activity of COPD Patients and non-COPD controls
Conference proceeding

The Utility of Shapelets for Analyzing Physical Activity of COPD Patients and non-COPD controls

Yuan An, Siling Chen, Nicholas Locantore, Matthew Allinder, Divya Mohan and Russell Bowler
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 1023-1030
Nov 2019
DOI: https://doi.org/10.1109/BIBM47256.2019.8983222

Abstract

COPD Physical Activity Shapelets Time Series Clustering
Physical activity is an attractive endpoint for novel therapies in Chronic Obstructive Pulmonary Disease (COPD). However, a deep understanding about COPD physical activity patterns and disease severity is lacking. In this research, we study the physical activity patterns for 184 individuals with and without COPD from a single center in the COPDGene cohort. These subjects participated in a 3-week observational study wearing wrist-worn accelerometers for collecting physical activity data. Our exploratory data analysis finds using the whole range of activity data is insufficient for patient clustering. Alternatively, we use shapelets, small and local sub-sequences, to better capture patients' behaviors in different groups. We develop a length-bound heuristic algorithm for choosing the subset that has the best clustering result. The study shows the potentials of using shapelets for helping providers in assessing COPD patients' status.

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