Modeling Aircraft Noise Induced Sleep Disturbance

Tuesday, December 10, 2013


Modeling Aircraft Noise Induced Sleep Disturbance. S. McGuire and P. Davies. A PARTNER Project 24 report. December 2013. Report No. PARTNER-COE-2013-004 Download (pdf 10MB)

One of the primary impacts of aircraft noise on a community is its disruption of sleep. Aircraft noise increases the time to fall asleep, the number of awakenings, and decreases the amount of rapid eye movement and slow wave sleep. Understanding these changes in sleep may be important as they could increase the risk for developing next-day effects such as sleepiness and reduced performance and long-term health effects such as cardiovascular disease. There are models that have been developed to predict the effect of aircraft noise on sleep. However, most of these models only predict the percentage of the population that is awakened. Markov and nonlinear dynamic models have been developed to predict an individual’s sleep structure during the night. However, both of these models have limitations. The Markov model only accounts for whether an aircraft event occurred not the noise level or other sound characteristics of the event that may affect the degree of disturbance. The nonlinear dynamic models were developed to describe normal sleep regulation and do not have a noise effects component. In addition, the nonlinear dynamic models have slow dynamics which make it difficult to predict short duration awakenings which occur both spontaneously and as a result of nighttime noise exposure. The purpose of this research was to examine these sleep structure models to determine how they could be altered to predict the effect of aircraft noise on sleep. Different approaches for adding a noise level dependence to the Markov Model was explored and the modified model was validated by comparing predictions to behavioral awakening data.   


To determine how to add faster dynamics to the nonlinear dynamic sleep models it was necessary to have a more detailed sleep stage classification than was available from visual scoring of sleep data. An automatic sleep stage classification algorithm was developed which extracts different features of polysomnography data including the occurrence of rapid eye movements, sleep spindles, and slow wave sleep. Using these features an approach for classifying sleep stages every one second during the night was developed. From observation of the results of the sleep stage classification, it was determined how to add faster dynamics to the nonlinear dynamic model. Slow and fast REM activity are modeled separately and the activity in the gamma frequency band of the EEG signal is used to model both spontaneous and noise-induced awakenings. The nonlinear model predicts changes in sleep structure similar to those found by other researchers and reported in the sleep literature and similar to those found in obtained survey data. To compare sleep disturbance model predictions, flight operations data from US airports were obtained and sleep disturbance in communities was predicted for different operations scenarios using the modified Markov model, the nonlinear dynamic model, and other aircraft noise awakening models. Similarities and differences in model predictions were evaluated in order to determine if the use of the developed sleep structure model leads to improved predictions of the impact of nighttime noise on communities.