Aircraft noise effects on sleep: a systematic comparison of EEG awakenings and automatically detected cardiac activations.

TLDR: An algorithm for the automatic identification of cardiac activations associated with cortical arousals, which uses heart rate information derived from a single electrocardiogram (ECG) channel, may be used as estimates for EEG awakenings.

Abstract: OBJECTIVES: Polysomnography is the gold standard for investigating noise effects on sleep, but data collection and analysis are sumptuous and expensive. We recently developed an automatic algorithm for the identification of cardiac activations associated with cortical arousals, which uses heart rate information derived from a single electrocardiogram (ECG) channel (Basner et al. 2007a). We hypothesized that cardiac arousals can be used as estimates for EEG awakenings. METHODS: Polysomnographic EEG awakenings and automatically detected cardiac activations were systematically compared using laboratory data of 112 subjects (47 male, mean ± SD age 37.9 ± 13 years), 985 nights and 23,855 aircraft noise events (ANEs). RESULTS: The overall agreement was higher in control (81.9 %) compared to noise nights (76.4 %). However, if corrected for chance expected agreement according to Landis and Koch (1977), agreement was higher in noise (к=0.60) compared to control nights (к=0.33), representing “moderate to substantial” and “fair” agreement respectively. The probability of automatically detected cardiac arousals increased monotonously with increasing maximum sound pressure levels of ANEs, exceeding the probability of EEG awakenings by up to 18.1 %. If spontaneous reactions were taken into account, exposure-response curves were practically identical for EEG awakenings and cardiac arousals. CONCLUSIONS: Automatically detected cardiac arousals can be used as estimates for EEG awakenings. This inexpensive, objective, and non-invasive method facilitates large scale field studies on the effects of traffic noise on sleep. More investigations are needed to further validate the ECG algorithm in the field and to investigate interindividual differences in its ability to predict EEG awakenings.