PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Traffic noise and its impact on sleep depth measured by theodds ratio productMichael SMITH1; Magdy YOUNES2; Daniel AESCHBACH3; Uwe MÜLLER3; Mathias BASNER1
1

Unit for Experimental Psychiatry, Division of Sleep and Chronobiology, Department of Psychiatry,
Perelman School of Medicine, University of Pennsylvania, USA2
3Sleep Disorders Center, University of Manitoba, Canada

Dept. of Sleep and Human Factors Research, Institute of Aerospace Medicine,German Aerospace Center, Germany

Abstract
Traffic noise can lead to cortical and autonomic activation, disrupt sleep and impair physical and mental
restoration. We used the odds ratio product (ORP), a validated continuous measure of sleep depth based on
automatic analysis of physiologic sleep data in 3-second epochs, to investigate temporal changes of sleep in
response to nocturnal noise events. Seventy-two healthy participants slept for 10 nights in a laboratory, during
which we measured sleep with polysomnography. In 8 nights, participants were exposed to 40, 80 or 120
road, rail and/or aircraft noise events at 45-65 dB LAS,max. Event-related change of ORP relative to pre-event
baseline was analysed with linear mixed models. ORP increased during aircraft, road and rail noise, reflecting
reduced sleep depth and quality, and there was a greater response to road and rail noise than to aircraft noise.
The clinical relevance of event-related elevations of ORP is currently unknown, and warrants furtherinvestigation.
Keywords: Traffic noise, Sleep, Laboratory study, Odds ratio product, Road, Rail, Aircraft

1. Introduction
Sufficient sleep is a critical component of good physical and mental health. Nocturnal traffic noise
can impair physiologic and subjective sleep, by causing cortical awakenings and self -reported sleep
disturbance (1). In the short-term, noise-induced sleep disturbance can cause impaired mood andcognitive performance and increased daytime sleepiness (2, 3). In the longer term, nocturnal traffic
noise has been positively associated with elevated blood pressure and incident cardiovascular disease(4-6). Consequently, sleep disturbance is believed to be the most harmful non -auditory effect ofenvironmental noise (7), and recently the World Health Organization strongly recommended morestrict nighttime regulations for road, rail and aircraft noise (8).
The “gold standard” for physiologic sleep measurement is polysomnography (PSG), which
involves recording of electrical brain activity using scalp electroencephalography (EEG), eyemovements via electrooculography (EOG) and muscle activity via submental electromyography(EMG). A major shortcoming of research into the effects of noise on sleep is that, according to current
guidelines (9), PSG data are scored in 30-second epochs. This is a hangover from when EEG traces
were plotted on 300 mm paper at a paper speed of 10 mm/s, thus a single page would reflect 30 s of
EEG activity (10). The EEG activity occupying the majority of the 30-second epoch determines thediscrete sleep stage that is assigned, either wake, rapid eye movement (REM) sleep or the non -REM
sleep stages N1, N2 or N3, representing progressively deeper sleep (9). Any given 30 s epoch cantherefore contain EEG activity indicative of a differentially scored sleep stage, providing thesepatterns are <15 s in duration. In this way, classical sleep scoring may not capture short duration yet
1michael.smith@pennmedicine.upenn.edu; basner@pennmedicine.upenn.edu

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potentially biologically relevant changes in sleep activity induced by noise .
The odds ratio product (ORP) is a continuous measure of sleep depth based on alpha, b eta, thetaand delta activity in the EEG (11), where the minimum value of 0 means definitely asleep, and themaximum value of 2.5 means definitely awake. This ORP measure has a number of advantages overtraditional sleep scoring for assessing the effects of noise. As a continuous metric, changes in ORP
reflect alterations of sleep depth at a finer resolution than is possible using onl y the 5 discrete stages
described in the current guidelines (9). Furthermore, the ORP is scored in 3-second epochs, which isshorter than the duration of many traffic noise events, so temporal changes of sleep within the periodof a noise event can be tracked, for instance as a function of the sound pressure level of the event.
Finally, ORP is scored using an automated algorithm. Although previou s attempts to score sleep
digitally have correlated poorly with manually scored sleep, such efforts have generally attempted toreplicate the current scoring of the PSG data into the 5 discrete stages. Additionally, when sleep isscored manually there can be significant intra- and inter-scorer variation despite identical scoring
rules (12), whereas automated ORP scoring precludes such subjectivity. Because of the advantages
offered by ORP, we reanalysed data from a previously reported laboratory study into the effects ofroad, rail and aircraft noise sleep (13) using event-related ORP measures.

2. Methods
2.1 Participants and study procedure
The methods are described in detail elsewhere (13), and are only summarised here. Seventy-two
participants (mean±SD 40.3±13.5 years, 40 women) were recruited for a laboratory study. They werehabitually good sleepers with a habitual mean bedtime of 23:09 and rise time of 07:01 and did not
suffer from self-reported sleep apnoea, loud snoring or symptoms associated with restless legs
syndrome or periodic limb movement disorder. They all had normal age-related hearing determinedwith audiometry. Chronotype was measured using the German language version of the Morningness Eveningness Questionnaire (14). The local ethics committee reviewed and approved the study.
Participants provided informed consent prior to the start of the study and could discontinue at any
time without explanation.
Participants slept for 10 consecutive nights in the laboratory. The first night served as an adaptation
period only, and was not included in any analyses. Nights 2-10, summarised in Table 1, consisted of
8 nights with 40, 80 or 120 noise events and a quiet control night, in a randomised order. Within noise
exposure nights, we varied the maximum sound pressure level of events between 45-65 dB L AS,max.
Noise events also had variations in noise onset rate and duration, as summarised in Table 2.
Table 1 Summary of the noise scenarios in different study nights.
LAEq: Equivalent A-weighted sound pressure level over the 8-hour nighttime period

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Number ofLAEq (dB)NightNoise typesControl-030.0AI

Aircraft4039.7RORoad

40

36.9

RA

Rail4039.7

AIRO

Aircraft, Road

80

41.2

AIRA

Aircraft, Rail

80

42.5

RORARoad, Rail80

41.2

RORORoad80

39.7

AIRORA

Aircraft, Road, Rail

120

43.3

noise events (n)Table 2 – Temporal acoustic properties of noise events for different traffic modes.
SPL: Sound Pressure Level. SD: Standard Deviation.
Aircraft.

Road.

Rail.

Mean (SD; range)

Mean (SD; range)

Mean (SD; range)

SPL rise time (dB/s)

3.6 (1.1; 1.2-5.8)

6.3 (1.9; 3.1-13.6)

7.1 (2.6; 2.3-12.7)

Duration (s)66.0 (16.6; 36.8-109.5)

20.5 (7.2; 9.0-38.1)

25.9 (7.9; 14.0-46.4)

Variable

We measured sleep with PSG, employing central EEG derivations (C3 -M 2, C4 -M 1 ), EOG and EMG
electrodes. EEG records electrophysiological activity in the cerebral cortex, EOG records eyemovement, and EMG records muscle movement. These data were used to calculate the ORP in 3 second epochs, via an automated algorithm (11). A single researcher, who was blind to the study design,
manually inspected ORP data to identify artefacts and/or periods of poor data quality.
2.2 Data analysis
For every noise event in every person-night, we identified the maximum ORP that occurred during
the duration of that specific noise event. From this value, we subtracted the pre-event baseline ORP,
defined as the mean ORP in the 30 seconds before the onset of that particular noise event, thus giving
a value expressing the maximum event-related change of ORP relative to baseline (ΔORP max). We also
calculated the area under the ORP curve (ORP AUC ) in a 111-second window, corresponding to theshortest window length that would fully enclose the longest noise event. We calculated the expectednatural baseline variation in ΔORP max and ORP AUC by analysing forty 30-second sham events in thequiet control night at times corresponding to noise events in the 40-event noise nights.
We performed statistical analysis using IBM SPSS version 25. We analysed the effect of theindependent variable traffic mode on ORP outcomes in a repeated measures random intercept linear
mixed model, using a first-order autoregressive covariance structure with homogeneous variances. Wecalculated post-hoc comparisons between traffic modes using Bonferroni adjustments for multiple
testing. A p-value <0.05 was considered statistically significant.

3. Results
There was a significant main effect of traffic type on noise-induced ΔORP max (F(3,21391)=2054.7,p<0.0001). As shown in Figure 1, ΔORP max was significantly greater during noise from aircraft(p=0.013), road (p<0.0001) and rail (p<0.0001) than in noise-free sham periods. There was asignificantly higher ΔORPmax during road (p<0.0001) and rail (p<0.0001) than aircraft noise. Eventrelated ΔORP max did not significantly differ between road and rail noise (p=1.0).

Figure 1 Event-related maximum change in ORP relative to pre-event baseline (estimated marginal means).
Error bars: 95% confidence intervals.

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There was a significant main effect of traffic type on noise-induced ORP AUC (F(3,23650)=69.54,p<0.0001). As shown in Figure 2, ORP AUC was significantly greater following noise from aircraft(p<0.0001), road (p<0.0001) and rail (p<0.0001) than in noise-free sham periods. There was asignificantly higher ORP AUC following road (p<0.0001) and rail (p<0.0001) than aircraft noise. Eventrelated ORP AUC did not significantly differ between road and rail noise (p=1.0).

Figure 2 Event-related area under the ORP curve in a 111s window (estimated marginal means).
Error bars: 95% confidence intervals.

4. Discussion
Noise from aircraft, road and rail led to elevations of ORP, reflecting reduced sleep quality andchanges to less deep, more unstable sleep depth (11). A greater physiologic response following roadand rail noise than aircraft noise is in good agreement with analysis of the current data using more
traditional methods of sleep and arousal scoring (13). While these results are not especially novel interms of demonstrating a sleep-disrupting effect of noise (1), they do demonstrate that ORP is asuitable method for assessing event-related effects of traffic noise on sleep, which can offer certain
advantages in future research in the field. Among these advantages are that a more graduated measureof sleep depth allows for identification of subtle (yet potentially relevant from a health perspective)
alterations of sleep, and a 10-fold increase in temporal precision compared to traditional 30 -second
epochs which will aid in uncovering the dynamics of noise-induced sleep disruption. A furtheradvantage is the high degree of automation in the ORP scoring process, rather than the manual sleep
scoring currently recommended (9), which decreases analysis time and mitigates individual error.
In further work, we will examine the effects of noise level, number of noise events, sleep stage,sex, age and sleep spindle rate on noise-induced changes in ORP. We will also investigate thesuitability of ORP as an indicator of individual trait-like characteristics including chronotype andnoise sensitivity, and examine the ability of ORP indices to predict the impact of sleep disruption on
subjective self-assessment of sleep and waking psychomotor and cognitive performance.
4.1 Limitations
There were 120 different noise events in total, 40 for each traffic mode (8 different recordings,
each at 5 different sound pressure levels). However, the distribution of these noise events in thislaboratory study may not be truly representative of traffic patterns in the real world. Furthermore, thenoise recordings were made in 2004, and vehicles that are more modern may have different acousticalcharacteristics.
As a laboratory study, the results may not translate directly to response in more naturalistic settings.
The participants were generally healthy, whereas in the field the disrupting effects of noise may behigher among vulnerable populations and residents with pre-existing sleep disorders. Furthermore,
there may be at least some degree of habituation among chronically noise-exposed individuals, which
would not be reflected in the data presented here.

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5. Conclusions
Traffic noise, already established as having adverse effects on sleep, accordingly induced
elevations of the odds ratio product of sleep, reflecting less deep, more unstable sleep. This response
was higher for road and rail than aircraft noise. The clinical relevance of event-related elevations ofORP is currently unknown, and warrants further investigation.

ACKNOWLEDGEMENTS
The study was performed at and internally funded by the German Aerospace Center (DLR). We
thank Jad Nasrini, Sarah Rocha, Makayla Cordoza and Marc Kaizi-Lutu for fruitful discussions during
data analysis and manuscript preparation.

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