This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Methodology
Kenan Li
University of Southern California
Corresponding Author
ORCiD: https://orcid.org/0000-0002-4641-6699
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
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, maximums) to characterize more detailed features of high-frequency time-series data.
Methods
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 for sequential data that optimally aligns interior patterns, both as the similarity measure and for training the neural network.
Results
We applied DTW-SOM to a panel study monitoring indoor and outdoor residential environmental exposures for 10 patients with asthma from 7 households near Salt Lake City, Utah; each patient was followed for up to 373 days. Compared to other SOM algorithms using Euclidean distance, the DTW-SOM algorithm maintained the topological properties of the input time series and generated more detailed diurnal patterns. We observed seasonal patterns in outdoor temperature and distinct patterns of indoor peak PM2.5 exposure, which was likely linked to both combustion sources and days with increased inhaler usage.
Conclusions
The new algorithm, DTW-SOM, better preserved the topology relationship of time-series data and better summarized time-series patterns as compared to the original version of SOM.
Keywords
Dynamic Time Warping, Self-Organizing Map, time series clustering, pattern discovery, asthma
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
View the published article at Scientific Reports.
Version 1
Posted 09 Oct, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Methodology
Kenan Li
University of Southern California
Corresponding Author
ORCiD: https://orcid.org/0000-0002-4641-6699
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
View the published article at Scientific Reports.
Version 1
Posted 09 Oct, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Scientific Reports.
Background
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, maximums) to characterize more detailed features of high-frequency time-series data.
Methods
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 for sequential data that optimally aligns interior patterns, both as the similarity measure and for training the neural network.
Results
We applied DTW-SOM to a panel study monitoring indoor and outdoor residential environmental exposures for 10 patients with asthma from 7 households near Salt Lake City, Utah; each patient was followed for up to 373 days. Compared to other SOM algorithms using Euclidean distance, the DTW-SOM algorithm maintained the topological properties of the input time series and generated more detailed diurnal patterns. We observed seasonal patterns in outdoor temperature and distinct patterns of indoor peak PM2.5 exposure, which was likely linked to both combustion sources and days with increased inhaler usage.
Conclusions
The new algorithm, DTW-SOM, better preserved the topology relationship of time-series data and better summarized time-series patterns as compared to the original version of SOM.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Loading...