Finger pulse wave amplitude response during sleep to environmental noise
Branko ZAJAMSEK(1) , Gorica MICIC(1) , Kristy HANSEN(1) , Peter CATCHESIDE(1)(1) Adelaide Institute for Sleep Health, Australia, branko.zajamsek@ﬂinders.edu.au

Abstract
Changes in ﬁnger pulse wave amplitude (FPWA) are a sensitive marker of autonomic and electro-encephalographic(EEG) activation responses evoked by relatively high level acoustic stimuli presented during sleep. FPWA responses could therefore be a particularly useful marker of physiological activation responses and sleep fragmentation caused by environmental noise during sleep. In this study we explored the FPWA response evoked byecologically relevant environmental noise exposures at moderate sound pressure levels (SPLs) up to 48 dB(A)during sleep. Twenty-three healthy participants took part in two noise exposure nights in a sleep laboratory,
separated by a recovery week. We found a signiﬁcant increase in FPWA response occurrence probability after noise onset at levels down to 33 dB(A). The size of evoked FPWA responses was small and similar to thesize of spontaneous responses irrespective of noise level. These data support that FPWA responses are a sensitive marker of physiological disturbances to environmental noise during sleep, although the physiological andpotential clinical signiﬁcance of frequent autonomic activation responses remains to be determined.
Keywords: Arousal, pulse wave amplitude, environmental noise disturbance, effects of noise

1

INTRODUCTION

Environmental noise has the potential to delay, reduce and fragment sleep via increased wake time, more frequent awakenings and micro-arousals. This can result in impaired mental and physical functioning and wellbeing [1]. Standard measures of sleep disturbance rely on traditional sleep scoring criteria. These use 30–second
data epochs to classify sleep stages, and simple counts of full-awakenings and more frequent shorter transient
micro-arousal events visible in the electro-encephalogram (EEG) [2]. Brief arousals fragment sleep and degrade
sleep quality, contributing to subsequent daytime sleepiness [5]. However, EEG based criteria reﬂect only highlevel cortical activation responses in a hierarchy of autonomic sensory and physiological activation processes
responsive to sensory disturbances throughout sleep. These reﬂect brainstem sensory and reﬂex processes that
regulate breathing and cardiovascular activity in response to disturbances that may arise during sleep and require rapid re-engagement of high cardio-metabolic and brain activity. Amongst the most prominent of thesereﬂexes is a powerful peripheral vasoconstriction response, which is the main element of a blood pressure surgethat accompanies arousals from sleep. This response can very rapidly shunt blood to the heart, lungs, brain
and activated muscles most needed for coordinated survival responses. This blood shunt can be easily detectedfrom a transient reduction in ﬁnger photoplethysmogram pulse wave amplitude (FPWA) and be evoked by astimuli such as noise [7]. Thus, FPWA is potentially a very useful marker of autonomic activation responses
to sensory disturbances, and may be a more sensitive marker of physiological sleep disruption than traditionallyscored EEG arousals. However, previous research investigating FPWA responses to acoustic stimuli used either
pure tones [4] or environmental noise [6] at relatively high sound pressure levels (SPLs) with respect to World
Health Organisation’s maximum recommended night-time indoor SPL of 45 dB(A) [8]. Therefore, the aim of
this pilot study was to investigate FPWA responses to environmental noise at more realistic night-time levels ofnoise exposure during sleep.

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2

METHODOLOGY

2.1 Participants and experimental conditions
Twenty-four healthy participants (11 males, mean (M) = 26.5 years, standard deviation (SD) = 16.4, age range:
18-70, 13 females, M = 21.7 years, SD = 1.2, age range: 19-23) were recruited for a two-night doubleblind, randomized and counter-balanced protocol. The study was approved by the Flinders University Social
and Behavioural Research Ethics Committee (SBREC) and all participants provided informed written consent.
Participants were recruited from the general population via social media and posters placed on University noticeboards, and were eligible for participation if they self-reported good health, and sleep of adequate quality andquantity as measured by the Insomnia Severity Index, Epworth Sleepiness Scale and Pittsburgh Sleep Quality
Index.
Auditory acuity was assessed by an audiologist using a manual threshold determination test to conﬁrm participants’ normal hearing at frequencies between 125–8000 Hz. Eligible participants were invited to attend two
overnight laboratory sleep studies spaced one week apart to allow sleep recovery, and provided with a sleep
diary and watch-size activity monitor to conﬁrm normal sleeping patterns in the week prior to the overnightin-laboratory studies. Participants’ self-determined habitual lights out and wake-up times were obtained by averaging sleep diary responses across all days reported. In the morning after each laboratory study, participantsalso responded to self-report questions assessing the perception of their sleep in the laboratory.
2.2 Sleep and FPWA measurement
During each study night, participants were set-up with polysomnography (PSG) electrodes to record electroencephalograms (EEG; F3, F4, C3, C4, Cz, O1 and O2), electro-oculograms (EOG; E1 and E2), chin electromyogram (EMG), leg movements, the electrocardiogram (ECG), respiratory signals (nasal cannula pressure
and thermistor, and chest and abdominal motion) and pulse-oximetry recordings. These signals were recorded
using Compumedics GRAEL v2 (Abbotsford, Vic, Australia) data acquisition system at a sampling rate between
256 Hz (pulse-oximetry) and 512 Hz (EEG, EOG, EMG, ECG). These data were subsequently used for sleep
staging and arousal scoring. To synchronise sleep and acoustic systems, a trigger signal for noise delivery indication was recorded using National Instruments 9234 module at a sampling rate of 8192 Hz on the acoustic
system and the maximum DC input channel recording rate of 64 Hz on the Grael system.
2.3 Noise intervention
A battery of block-randomised noise stimuli were presented at least 5 minutes into established stage 2 sleep at
the start of the protocol and after at least 1 minute of stage 2 sleep on any subsequent return to sleep afteran awakening (>15 sec arousal). One night consisted of 20 second noise stimuli (N20 ) and the other used 180
second stimuli (N180 ). During both nights there was a 20 second pause between consecutive noise deliveries.
The N20 noise battery was played at overall SPLs of 33–48 dB(A) in 3 dB increments giving 6 distinct levels
and included wind farm noise with amplitude modulation (WFN AM), wind farm noise without amplitude
modulation (WFN NoAM), wind farm noise with amplitude modulation and mid- to high-frequency content(Swish), trafﬁc noise recorded next to a road (TN short-range) and trafﬁc noise recorded inside a suburbanhouse in a room with an open window (TN long-range). The N180 noise battery contained 33, 38 and 43
dB(A) levels of TFN short-range and WFN AM. Both nights also contained a background noise only conditionat 23 dB(A) as a baseline control exposure.
2.4 FPWA response detection algorithm
An algorithm was developed in the MATLAB programming language to detect and characterise FPWA responses. The FPWA response is characterised by a distinguishable rapid drop, over a few seconds, from thepreceding baseline followed by a more gradual return back to baseline over 10–30 seconds. The main responsefeature calculated was the area under the curve (AUC) from baseline in arbitrary units (au; given that the photo-3019

plethysmogram provides an un-calibrated light absorbance signal) in order to quantify both response magnitude
and duration within a single summary measure. Any FPWA response curve associated with a characteristic rapid
drop followed by a more gradual return to baseline and an AUC exceeding 200 au before the return to baseline
was classiﬁed as a vasoconstriction response. This is an arbitrary cut-off.
2.5 Statistical analysis
Cumulative event rate curves for each SPL were estimated using the Kaplan–Meier product-limit method. The
overall cumulative risk was compared between noise samples at the various SPLs and the control using a
two-sided log-rank test. The hazard ratios and conﬁdence intervals for SPLs, relative to the control group,
were estimated with the use of a Cox proportional hazards model. Cumulative event rates at ﬁxed time points(landmark points) and log-transformed 95% conﬁdence interval were derived from the Kaplan–Meier estimates.
The landmark point at 5 seconds (LM5 ) was chosen based on visual inspection of the cumulative event curves
which showed a clear rate change at that time. The analysis was performed over 40 and 200 seconds-long time
windows for N20 and N180 , respectively, which is in accordance with the length of noise samples and pause
between the noise deliveries on each night. FPWA response events were censored if they occurred after 40 or
200 seconds from the onset and if the FPWA response size was less than 200 au. Linear Mixed Model analysis
was used to examine whether chronological night and noise duration (20-sec vs. 3-min) had an overall effecton sleep efﬁciency (i.e., total sleep time/total time in bed, expressed as percentage) and subjective sleep quality.
The data are presented as mean ± SEM and 95% conﬁdence limits. A value of P <= 0.05 was considered
statistically signiﬁcant.

3

RESULTS

3.1 Sleep architecture
No signiﬁcant interaction effects of chronological night and noise sample duration (F(1, 42) = 0.23, P = 0.633),
and no signiﬁcant main effects of chronological night or noise duration (F(1, 42) = 2.48, P = 0.123 and F(1,42)= 1.05, P = 0.311), were observed. Young adults aged 20 – 30 years (i.e., analogous to the present study’s
sample) in the general population have a sleep efﬁciency of 85 – 95%, suggesting that sleep in the present
study was comparatively normal, albeit on the lower end of the range. Subjective sleep quality ratings reﬂected“fair” sleep, which was “a little worse than usual”. These results suggest that participants who were speciﬁcallyselected as good sleepers for this study, exhibited generally good sleep quality that was perhaps somewhat
reduced in the laboratory and noise exposure context.
3.2 Number of noise presentations
Tables 1 and 2 show the number of delivered noise samples during stage 2 sleep for N20 and N180 , respectively.
The distribution of noise types at various levels is fairly even for nights with overall more noise samples being
delivered during N20 due to the shorter noise samples.
Table 2. Number of noise and control deliveries for N180 .
SPL, dBA
23
Background noise (Control)
TN short-range
WFN AM

3020

33

38

43

62
64

68
63

65
65

70

Table 1. Number of noise and control deliveries for N20 .
SPL, dBA
23
Background noise (Control)
Swish
TN short-range
TN long-range
WFN AMWFN noAM

33

36

39

42

45

48

102
113
120
115
107

110
103
121
121
118

107
103
106
104
108

122
107
113
107
119

118
102
107
110
117

116
98
110
110
120

110

3.3 FPWA response probability
These results are presented using a Kaplan-Meier cumulative incidence curves which show the cumulative probability of FPWA response occurrence as a function of time. A vertical increase in these curves indicates event
occurrence and furthermore the curves can never decrease as the cumulative number of occurred events canonly remain stable or increase over time. The vertical tick lines on the curves indicate censored events whichin this case are FPWA responses smaller than 200 au. At time zero, the cumulative incidence is zero as no
event has occurred yet and reaches 1 at some time >0 if all events have occurred. Cumulative incidence curves
are accompanied by risk tables with the information about the number of events at risk at selected time points.
Number of events at risk decreases with increasing time and eventually reaches 0 (at which point the cumulative
incidence curve would then reach 1) if the time window is sufﬁciently long.
Figure 1 shows the cumulative incidence of FPWA responses for SPLs and control over N180 . The control curveshows a steady near constant rate of rise likely reﬂecting quite frequent spontaneous FPWA response occurrencein the absence of noise. For the FPWA responses evoked by noise, the incidence rate increased rapidly in theﬁrst 5 seconds from noise onset and then abruptly returned to the spontaneous rate. This strongly supports thatevoked FPWA responses occur shortly after the noise onset, and is consistent with previous observations [4].
At LM5 , the evoked FPWA responses were 2–3 times, and statistically signiﬁcantly, more likely to occur as in
the control group for 33, 38 and 43 dB(A), respectively.
Similar trends were also observed for N20 and are shown in Figure 2. However, for N20 only the 48 dB(A)level was different from the control condition, although there was a strong trend for a difference with 45 dB(A).
To investigate for potential habituation of repeated brief noise exposures or time of night effects on N20 , theFPWA responses were divided in half by the median of their time occurrences across the night as measured
from the ﬁrst noise delivery. Figure 3 shows cumulative incidence curves for two halves of all FPWA response
occurrences across the night. In the ﬁrst half of all response occurrences shown in Figure 3A, all SPLs above
40 dB(A) were signiﬁcantly different from the control condition. However, in the second half of all noise
exposures across the night only the loudest noise evoked a FPWA response above the spontaneous rate (Figure
3).
Figure 4 shows group-averaged FPWA responses for N20 and N180 in sub-ﬁgures A and B, respectively. Averaging was done over all responses that occurred within 5 seconds from the noise onset to capture evoked
responses. The responses were aligned along their onsets as identiﬁed by the algorithm. The maximum drops
or the size of the responses are similar among different levels and control.

4

DISCUSSION AND CONCUSSIONSThis paper reports an exploratory analysis of FPWA responses evoked by realistic levels of environmental noise
during sleep. This response was observed at all sound pressure levels at variable rates of occurrence, dependingon the SPL and duration. During protocols with shorter 20-sec noise exposures only 45 and 48 dB(A) SPLs

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Figure 1. Cumulative event incidence rate curves for FPWA during N180 with the landmark point (LM5 ) at 5
seconds during stage 2 sleep and 95% CI denoted by shaded areas. The hazard ratio (HR) is evaluated withrespect to the control.
consistently exceeded the spontaneous rate of FPWA response occurrence. Despite fewer presentations withlonger 180-sec noise exposures all SPLs examined, including 33 dB(A), exceeded the spontaneous rate. Givenstrong evidence for reduced incidence rate in the second compared to the ﬁrst half of all exposures on theN20 night these effects are strongly suggestive of important habituation or perhaps sensory fatigue effects withrepeated short noise exposures.
Noise duration had no effect on either the time of occurrence or response magnitude of the FPWA response.
A sharp rise was evident in cumulative incidence curves shortly after noise onset irrespective of noise duration.
These ﬁndings strongly suggest that FPWA responses are governed by noise onset effects (i.e., sudden change
in SPL) rather than exposure length, and elicit a probabilistic “all-or-none” reﬂex response largely independentof SPL above background in the 33–48 dB(A) range examined in this study. Noise frequency content had no
signiﬁcant effect on FPWA responses (results not shown) somewhat contrary to previous ﬁndings [6]. However,
SPL variations within noise samples (such as amplitude modulation (AM) in wind farm noise) in our study
were <12 dB while previous studies [6] used noise with up to 40 dB in SPL variations.

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Figure 2. Cumulative incidence rate curves for FPWA during N20 with the landmark point at 5 seconds (LM5 )during stage 2 sleep and 95% CI denoted by shaded areas. The hazard ratio (HR) is evaluated with respect tothe control. Statistics are shown only for levels showing statistically signiﬁcant differences or a strong trend fora difference.
The lack of FPWA responses at lower SPLs observed during N20 may reﬂect habituation of peripheral vasoconstriction system responses to brief noise exposures. Thus, it is plausible that habituation effects were reduced
during N180 at 33 dB(A) since the total number of responses is approximately 3–4 times less than during N20(see risk tables in Figures 1 and 2). Apart from habituation, sleep architecture and different SPLs between N20
and N180 and carry-over effects could also contribute towards observed differences. Sleep architecture varies
across the night such that deep sleep is predominant in the ﬁrst half of the night where cortical arousal responses are much less likely suggesting that brainstem sensory information transmission to higher cortical levelsin the brain is more strongly gated to protect sleep in deep sleep. Given that deep sleep reduces and REM
sleep increases during the course of the night changing sensory gating effects might have contributed to timeof-night effects. Whilst we controlled for sleep stage effects by only examining noise presentations within stage
2 sleep, this does not rule out time-dependent sensory processing changes not captured by traditional sleep
scoring. Further studies would be useful to examine stage and potential habituation or sensory fatigue effects in

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A

B

Figure 3. Cumulative incidence rate curves for FPWA response during N20 with the landmark point at 5
seconds (LM5 ) for all FPWA responses divided in half by the median of their occurrence time across the night.
A) shows responses occurring from the ﬁrst noise onset up until the median and B) shows responses occurringfrom the median onwards until the last noise onset of the night. Hazard ratios (HR) evaluated with respect tothe control are only shown for where there were signiﬁcant effects. Shaded areas indicate 95% CI.

more detail.
The absence of any differences in FPWA response magnitude with different SPLs and duration is consistent witha remarkably sensitive all-or-none cardiovascular reﬂex response to small noise disturbances. The overall drop in
FPWA was approx. 20% from the baseline, consistent with previous observations [4]. This response is relatively
small in comparison to a response proceeding an awakening or a response to a loud tone (>80 dB(A)), which
can cause up to a 60% reduction in FPWA magnitude [4]. Thus, although we found no difference in response
magnitude between noise types (results not shown) the strength of peripheral vasoconstriction responses does
may only be important when stimulus intensity is sufﬁciently high (>48 dB(A)). Given these observations,the effects of other noise features on FPWA response magnitude in the higher range of sound pressure levels
relevant to environmental noise exposures also warrants further investigation. The physiological and clinicalsigniﬁcance of frequent cardiovascular activation responses is yet to be determined and clearly warrants further
research.

REFERENCES[1] Micic, G. et al. A Review of the Potential Impacts of Wind Farm Noise on Sleep. Acoust. Aust. 46, (2018).
[2] Rechtschaffen, A.; Kales, A. A manual of standardized terminology, techniques and scoring system for sleep
stages of human subjects. Electroencephalography and Clinical Neurophysiology (1968).
[3] Griefahn, B.; Brode, P.; Marks, A.; Basner, M. Autonomic arousals related to trafﬁc noise during sleep.
Sleep 31, 569–577 (2008).
[4] Catcheside, P. G.; Chiong, S. C.; Mercer, J.; Saunders, N.; McEvoy, R. D. Noninvasive cardiovascularmarkers of acoustically induced arousal from non-rapid-eye-movement sleep. Sleep 25, 797–804 (2002).

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A

B
140

140

FPWA, %

N20
FPWA response onset

N180
FPWA response onset

120

120

100

100

80

80

60

60

SPL

40

40

23 dBA (Control)
33 dBA
36 dBA

20

SPL

20

39 dBA

23 dBA (Control)

42 dBA

33 dBA

45 dBA0

38 dBA0

48 dBA
−20

−10

0

10

Seconds

2030

43 dBA
−20

−10

0

10

20

30

Seconds

Figure 4. Group averaged FPWA responses for A) N20 and B) N180 with 20 seconds pre-response baseline level
during stage 2 sleep. Shaded areas indicate 95% CI.
[5] Martin, S. E.; Wraith, P. K.; Deary, I. J.; Douglas, N. J. The effect of nonvisible sleep fragmentation on
daytime function. Am. J. Respir. Crit. Care Med. 155, 1596–1601 (1997).
[6] Di Nisi, J.; Muzet, A.; Ehrhart, J.; Libert, J. P. Comparison of cardiovascular responses to noise during
waking and sleeping in humans. Sleep 13, 108–120 (1990).
[7] Griefahn, B.; Brode, P.; Marks, A.; Basner, M. Autonomic arousals related to trafﬁc noise during sleep.
Sleep 31, 569–577 (2008).
[8] World Health Organization. Environmental noise guidelines for the European region. WHO: Geneva,
Switzerland, 2018.
[9] Basner M.; Samel A.; Isermann U.; Aircraft noise effects on sleep: Application of the results of a large
polysomnographic ﬁeld study. J Acoust Soc Am 2006;119:2772-84.
[10] Muzet A. Research on noise-disturbed sleep since 1978. In: Rossi G, ed. Noise as a Public Health Problem. Milano; Edizioni Tec- niche a cura del Centro Ricerche e Studi Amplifon. 1983, 883-93.

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