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Using kinematic driving data to detect sleep apnea treatment adherence
Anthony D McDonald1, John D Lee2, Nazan S Aksan3
1Texas A&M University, College Station, TX, USA.
Journal of Intelligent Transportation Systems
|October 23, 2018
Summary
Driving data can identify lapses in obstructive sleep apnea (OSA) treatment adherence. This approach may also help evaluate new treatments and detect drowsy driving patterns.
Area of Science:
- Medical informatics
- Transportation safety
- Behavioral science
Background:
- Driving behavior generates extensive data, previously limited to safety analysis.
- This data holds potential for identifying health conditions, such as obstructive sleep apnea (OSA).
Purpose of the Study:
- To investigate if driving behavior patterns can detect treatment adherence in OSA patients.
- To explore the utility of driving data for health monitoring and medication analysis.
Main Methods:
- Collected driving data and medication adherence from 75 OSA patients over 3.5 months.
- Transformed speed/acceleration data into symbolic patterns and frequencies using Symbolic Aggregate Approximation and sliding windows.
- Trained a random forest model to correlate driving patterns with treatment adherence.
Main Results:
- The random forest model successfully detected lapses in OSA treatment adherence.
- Key driving patterns associated with adherence included route decisions and potential drowsy driving indicators.
- Variable importance analysis highlighted specific driving behaviors linked to treatment compliance.
Conclusions:
- Driving data is a viable tool for monitoring OSA treatment adherence.
- This methodology can inform new treatment evaluations, medication side effect analysis, and improve drowsy driving detection algorithms.
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