Abstract

Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM2.5) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients …

Date

December 15, 2021

Authors

Kenan Li, Katherine Sward, Huiyu Deng, John Morrison, Rima Habre, Meredith Franklin, Yao-Yi Chiang, Jose Luis Ambite, John P Wilson, Sandrah P Eckel

Journal

Scientific reports

Volume

11

Issue

1

Pages

24052

Publisher

Nature Publishing Group UK