Logo image
Back
Highway Healthcare: How Naturalistic Driving Data Index Adherence to CPAP Therapy in Obstructive Sleep Apnea
Journal article   Open access   Peer reviewed

Highway Healthcare: How Naturalistic Driving Data Index Adherence to CPAP Therapy in Obstructive Sleep Apnea

Anthony D McDonald, John D Lee, Nazan S Aksan, Jeffrey D Dawson, Jon Tippin and Matthew Rizzo
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol.57(1), pp.1859-1863
09/2013
DOI: 10.1177/1541931213571415
PMCID: PMC4613796
PMID: 26500422
url
http://doi.org/10.1177/1541931213571415View
Open Access

Abstract

Drowsy driving is a major factor in many vehicle crashes around the world. Sleep disorders, such as obstructive sleep apnea (OSA), underpin many of these crashes. Continuous positive airway pressure (CPAP) therapy is an effective treatment for sleep apnea but it requires consistent use and is often rejected by OSA patients. Rejection of CPAP treatment creates a dangerous on-road environment for both OSA sufferers and the general public. Algorithms capable of detecting CPAP use and its effects on driving are integral to identifying and mitigating this danger. This work uses naturalistic kinematic driving data to develop an algorithm which can detect nightly CPAP abstinence and adequate CPAP use. Speed and lateral acceleration data were collected using a data recorder in participant's primary vehicle and CPAP data were collected by downloading adherence data from participant CPAP machines. The speed and acceleration data were reduced to a set of symbols using Symbolic Aggregate approximation (SAX) time-series analysis. The symbols were converted into a sequence frequency dataset using sliding windows of size 1 to 10 s with a 1 Hz sampling rate. A Random Forest classifier was trained on the data to create a classification algorithm. On a held aside testing set, the Random Forest algorithm correctly identified 71% of the instances and had an area under the receiver operating characteristic curve of 0.76. The variable importance of the algorithm suggested that kinematic patterns associated with common drowsy driver crash types were key features in the algorithm's prediction performance.

Details

Metrics

12 Record Views
Logo image