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|Year : 2012
: 14 | Issue : 61 | Page
|Critical appraisal of methods for the assessment of noise effects on sleep
Mathias Basner1, Mark Brink2, Eva-Maria Elmenhorst3
1 Unit for Experimental Psychiatry, Division of Sleep and Chronobiology, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
2 ETH Zurich, D-MTEC Public and Organizational Health, WEP H17, CH-8092 Zurich, Switzerland
3 Department of Flight Physiology, German Aerospace Center (DLR), Institute of Aerospace Medicine, 51170 Cologne, Germany
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|Date of Web Publication||19-Dec-2012|
Various sleep measurement techniques have been applied in past studies on the effects of environmental noise on sleep, complicating comparisons between studies and the derivation of pooled exposure-response relationships that could inform policy and legislation. To date, a consensus on a standard measurement technique for the assessment of environmental noise effects on sleep is missing. This would be desirable to increase comparability of future studies. This manuscript provides a detailed description of the sleep process, typical indicators of disturbed sleep, and how noise interferes with sleep. It also describes and discusses merits and drawbacks of five established methods commonly used for the assessment of noise effects on sleep (i.e., polysomnography, actigraphy, electrocardiography, behaviorally confirmed awakenings, and questionnaires). Arguments supporting the joint use of actigraphy and a single channel electrocardiogram as meaningful, robust, and inexpensive methods that would allow for the investigation of large representative subject samples are presented. These could be used as a starting point for the generation of an expert consensus.
Keywords: Actigraphy, awakening, health, noise, polysomnography, sleep
|How to cite this article:|
Basner M, Brink M, Elmenhorst EM. Critical appraisal of methods for the assessment of noise effects on sleep. Noise Health 2012;14:321-9
| Introduction|| |
The design stage of a study on the effects of noise on sleep involves many decisions that will influence, on the one hand, validity and generalizability of the findings and, on the other hand, methodological expense and therefore the costs of the study. In general, there is no perfect study design that maximizes all desirable attributes of a study. Rather, every single decision will influence some study aspects positively and others negatively.
One study design aspect of paramount importance concerns the technique for measuring sleep and the influences of noise on sleep. For example, polysomnography (see "polysomnography" section) allows for the detection of subtle physiological changes induced by noise. However, this measurement technique is somewhat disruptive and may therefore influence sleep itself. Moreover, its high methodological expense (and the associated costs) renders studies with large sample sizes impossible. It also very likely decreases participation rates and therefore generalizability of the findings.
Past studies on the effects of traffic noise on sleep applied a variety of sleep measurement techniques. Even if the same technique was used, differences in data analysis strategies complicate a comparison of different studies or pooling data from different studies. Berry et al.  noticed: "The literature review identified a number of key research studies each of which considered separately provides useful information regarding dose-effect relationships for acute or transient sleep disturbance against sound levels measured either inside or outside the bedroom. [...] Because different studies reported in the literature use a wide range of different experimental methods and dose and effect variables, this makes the different sets of findings very difficult to compare." Thus, a consensus on measurement technique and analysis strategy would be helpful to guarantee comparability between future studies. At the same time, studies on large representative population samples (probably including risk groups) are needed to inform policy and legislation.  This will require a cost-effective yet meaningful and methodologically sound measurement technique.
The "Sleep and the Effects of Noise on Sleep" section of this manuscript provides a detailed description of the sleep process, typical indicators of disturbed sleep, and how noise interferes with sleep. The "Discussion of Methods for the Assessment of Noise Effects on Sleep" section describes five different sleep measurement techniques (polysomnography, actigraphy, electrocardiography [ECG], behaviorally confirmed awakenings, and questionnaires) and discusses advantages and disadvantages of each method. The "Discussion" section discusses the findings of the previous two sections and gives recommendations for the choice of measurement technique for future studies on the effects of noise on sleep.
This manuscript focuses on the measurement of responses to nocturnal noise exposure (i.e., sleep disturbance), and not on the measurement of the exposure itself (i.e., the acoustical environment), although both are of paramount importance for the derivation of valid and precise exposure-response functions. Important considerations relative to noise measurements include (a) the type of sound level meters used, (b) the location of the sound level meter (outside, inside the bedroom, exact position of the microphones), (c) the recorded variables (with or without recording the actual sound files), (d) sample rate, and (e) synchronization with physiological measurements. For a more detailed discussion of the exposure assessment in studies on the effects of aircraft noise on sleep, the reader is referred to Basner (pp. 23-25). 
| Sleep and the Effects of Noise on Sleep|| |
Sleep is a complex human behavior, integrating manifold vital physiological processes (e.g., protein biosynthesis, excretion of specific hormones, memory consolidation) that, in a broad sense, serve recuperation and prepare the organism for the next wake period. The human organism recognizes, evaluates, and reacts to environmental sounds even while asleep.  As early as in 1939, Davis  stated, "The effectiveness of auditory stimuli during sleep may be no accident if we consider the general biological function of hearing in the role of watchman constantly on guard to signal danger".
Polysomnography, that is, the simultaneous recording of electrical brain activity (i.e., electroencephalography, or EEG), muscle tone (i.e., electromyography, or EMG), and eye movements (i.e., electrooculography, or EOG) remains the gold standard to measure sleep (see "polysomnography" section). According to specific conventions, , the recorded night is usually divided into 30-second epochs. Depending on EEG frequency and amplitude, specific patterns in the EEG, muscle tone in the EMG, and the occurrence of slow or rapid eye movements in the EOG, different stages of sleep are assigned to each epoch. Wake is differentiated from sleep. Sleep is divided into rapid eye movement (REM) sleep and non-REM sleep, which is again classified into light (stages N1 and N2) or deep sleep (stage N3, also called slow wave sleep [SWS]). SWS and REM sleep seem to be very important for restoration and memory consolidation during sleep. ,, Wake and N1, although physiological part of the sleep process, are typical indicators of disturbed or fragmented sleep, and they do not (or only very little) contribute to the recuperative value of sleep.  Even shorter activations (≥3 seconds) in the EEG and EMG (so-called arousals) that would not qualify to be scored as an awakening, can be detected in the polysomnogram.  These arousals are usually accompanied by activations of the autonomous nervous system (see below). ,
The Ascending Reticular Activating System (ARAS) is part of the body's arousal system, and is most active during wakefulness. It receives input from several sensory systems (among them the auditory) and relays this information, for example, to cardio-respiratory brainstem networks and through the thalamus to the cortex. The thalamus has a gating function, that is, based on sensory information and the current state of the central nervous system (CNS), information may be relayed to or withheld from the cortex.  If the information is passed on to the cortex, it may lead to a cortical arousal, that, if the subject is sleeping, may disturb or fragment sleep [Figure 1].
|Figure 1: Example of an EEG arousal according to definitions of the American Academy of Sleep Medicine.[6,20] An increase in EMG amplitude (with corresponding artifacts in the EEG) and an increase in EEG frequency are the defining elements of the EEG arousal. The EEG arousal is accompanied by a vegetative arousal (i.e., an increase in heart rate seen in the ECG channel). 36 s are printed in this record, and therefore the arousal lasts for approximately 12 s, which does not qualify the epoch to be scored as wake.[6,7] EEG: Electroencephalogram; EMG: Electromyogram; ECG: Electrocardiogram|
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Several important implications follow for the effects of noise on sleep
(1) The organism's reaction to noise is not based on an all-or-nothing principle (i.e., not every noise event will lead to a conscious awakening). Rather, the reaction is fine-graded ranging from (depending on the acoustic stimulus and the momentary state of the CNS) (a) no or minimal physiological reaction (i.e., not detectable with standard equipment), (b) to an isolated vegetative reaction (e.g., increase in heart rate and blood pressure), (c) to a cortical arousal of different degrees (subtle shift in EEG frequency, EEG arousal, sleep stage shift to a lighter sleep stage, sleep stage shift to stage wake), and (d) to a full cortical arousal with regaining of waking consciousness [Figure 2].
|Figure 2: Simplified scheme of the body's reaction to external stimuli (as aircraft noise)|
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It is generally accepted that stronger degree arousals (e.g., conscious awakenings) will have greater consequences for recuperation than those of lesser degree (e.g., vegetative arousals), especially since the former regularly include the latter, but not vice versa. For the same reason, in quiet nights conscious awakenings occur much less frequent than, for example, brief EEG arousals. However, this does not mean that arousals of lesser degree are of no consequence for recuperation. In fact, it is assumed that many short CNS arousals will fragment sleep and impair recuperation even without relevant changes in sleep macrostructure (i.e., total sleep time, distribution of sleep stages), ,, although the independence of both processes is still a matter of debate. , In the end, the belief that shorter cortical activations are important for recuperation lead to the definition of EEG arousals by the American Academy of Sleep Medicine (then called American Sleep Disorders Association) in 1992. EEG arousals are today routinely scored in sleep laboratories around the world. 
(2) CNS arousals are a physiological part of the sleep process and of no pathological consequence unless a certain physiological amount is exceeded (see red numbers given in [Figure 2] for spontaneous arousal frequencies in quiet nights).  As a multitude of external and internal stimuli regularly induce CNS arousals during sleep [Figure 3], the latter are unspecific (i.e., not specific for noise).
|Figure 3: Internal and external pathways to CNS arousals (modified, based on Raschke and Fischer 64)|
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This has several consequences. If a CNS arousal (of whatever degree) is observed in association with a noise event, one cannot be sure that this arousal was actually caused by the noise event, as it is possible that - by chance - it was induced by another external or internal stimulus at the same time. Therefore, only a certain fraction of CNS arousals will be attributable to the noise event, and there are different ways to calculate the magnitude of this fraction (see Brink et al.  for a detailed discussion). This will also affect the evaluation of the severity of one additional physiological reaction induced by noise. Clearly, one additional EEG arousal (with more than 100 spontaneous EEG arousals in undisturbed quiet nights) is very likely less harmful than one additional EEG awakening (20-25 in quiet nights) or one additional conscious awakening (1-5 in quiet nights).
All of the above have consequences for the choice of a sleep measurement technique. Comparable to diagnostic tests, the different measurement techniques differ in their sensitivity and specificity. A very sensitive measurement technique (like polysomnography) will pick up even subtle physiological changes like EEG arousals. However, as noted above, EEG arousals are not very specific indicators for noise-induced sleep disturbance as they occur spontaneously more than 100 times in each (quiet) night. A very specific measurement technique (like behaviorally confirmed awakenings, see below) may pick up events that otherwise occur only seldom spontaneously. However, other forms of CNS activation, that may have consequences for sleep recuperation, will be missed by this insensitive method.
Hence, a measurement technique may be characterized as "optimal" if it has a favorable balance between sensitivity and specificity, that is, if it detects all relevant noise-induced activations of the CNS. Unfortunately, there is no consensus among sleep researchers or noise effects researchers what exactly constitutes a relevant CNS activation. Guilleminault et al.  were able to demonstrate in a carefully designed study with, however, only six subjects that only cortical arousals were associated with increased sleepiness or reduced performance during the next day. Vegetative arousals alone did not lead to next day consequences. These results conflict with those of Martin et al,  who claimed that vegetative arousals alone would significantly impair recuperation. However, taking a closer look at their experimental procedure, it is probable that the procedure itself induced some cortical arousals and even changes in sleep macrostructure, so that cortical arousals may indeed be a prerequisite for next day consequences, whereas vegetative arousals alone may suffice to increase the long-term risk of cardiovascular disease. 
In the context of designing a field study on the effects of noise on sleep, this underlines the importance of defining a priori what is considered a relevant physiological or behavioral effect of noise. Basner et al,  argue for EEG awakenings as adequate indicators for noise-induced sleep disturbance because (a) EEG awakenings demonstrate a good balance between sensitivity and specificity (see above and [Figure 2]), (b) they are, in contrast to briefer EEG arousals, accompanied by prolonged increases in heart rate , that may play a role in the development of high blood pressure and cardiovascular disease, , and (c) waking consciousness may be regained due to longer EEG awakenings.  These awakenings may be recalled the next morning and affect subjective assessments of sleep quality and quantity. At the same time, noise-events perceived during wake periods can result in annoyance and may prevent the subject from falling asleep again, especially in the early morning hours.  However, as mentioned above, this does not mean that shorter EEG activations (or even more subtle shifts in EEG frequency) are without consequences. Moreover, it was shown that EEG arousals habituate to a lesser degree than EEG awakenings, and, in contrast to EEG awakenings, that they do not replace spontaneous EEG arousals between noise events.  Therefore, focusing on EEG arousals may add relevant information, especially in chronic exposure situations (like in field studies) or in study regions with low noise exposure levels. 
| Discussion of Methods for the Assessment of Noise Effects on Sleep|| |
Sleep can be assessed in several ways, ranging from questionnaire-based self-reports in the morning after nights with noise exposure to polysomnography, that is, the simultaneous measurement of EEG, EOG, and EMG (even more elaborate techniques like functional neuroimaging can currently not be performed in the field). The different measurement techniques differ in their sensitivity and specificity for detecting noise-induced sleep disturbances, in their invasiveness, in their methodological expense, and thus in monetary costs. In the following sections, the different measurement techniques are described and their advantages and disadvantages are discussed.
| Polysomnography|| |
Polysomnography is the simultaneous recording of at least the EEG, EOG, and EMG that are needed for sleep stage classification and arousal scoring. However, oftentimes (and certainly in clinical settings) additional sensors are applied to measure heart rate, movements of the rib cage and abdomen, limb movements, airflow, or esophageal pressure. According to specific conventions (International 10-20-system), electrodes are attached to the scalp and the skin of the face of the subject. The electrical potentials generated by the brain, chin muscles, and eye movements are amplified, converted into digital signals, and stored on digital media. The signals are later analyzed by trained personnel according to specific conventions (see above). ,
Polysomnography is the gold standard for measuring sleep, the evaluation of sleep structure and the degree of sleep fragmentation. As can be seen in [Figure 2], it is a method that covers most physiological aspects of sleep (with the exception of conscious awakenings, as we cannot tell with certainty from the polysomnogram whether a subject regained waking consciousness or not). It is thus a very sensitive method that will detect even subtle changes in sleep physiology. Additionally, the method itself is very well standardized.
EEG, EOG, and EMG electrodes and leads are somewhat disruptive, may influence sleep, and thus at least one night is usually required for adaptation.  The measurement instruments are expensive and fragile. The instrumentation and de-instrumentation of subjects is cumbersome and has to be done by trained personnel. EEG and EMG electrodes are sometimes affected by movements or excessive sweating of the subjects, which may render the analysis of (part of) the data gathered during the night impossible. Finally, sleep stage classification requires trained personnel and is known to have high inter- and intra-observer variabilities. ,, Automated sleep stage classification systems exist, but so far validation studies reached contradictory conclusions. 
| Actigraphy|| |
Actigraphs measure acceleration of body movements (in one or more dimensions), have the size of a watch, and are worn like wrist-watches (usually on the wrist of the nondominant arm). Some products have additional features, for example, light sensors measuring environmental light intensity (sometimes in different spectra), body position sensors, an event marker button (e.g., to signal lights out), or a display (e.g., for displaying clock time). Some devices even allow for sampling other physiological signals like the ECG, but this discussion shall focus on the basic feature of actigraphs to measure (wrist) movements during sleep. The devices usually sample at high rates internally (e.g. 256 Hz), but (user-defined) data storage rates are typically much lower (e.g., 1-2 values/min). Therefore, a 1 min or 30 s bin will store the degree of wrist movement in the respective period. Actigraphy was used in two large studies on the effects of aircraft noise on sleep in the vicinities of Heathrow  and Amsterdam Airport.  There are other methods related to actigraphy (e.g., seismosomnography  ) that measure whole-body movements during sleep.
Actigraphs are inexpensive and comparatively robust. After an initial orientation, subjects can wear the device for several days and nights unsupervised (i.e., the methodological expense is low). The movement activity data gathered with actigraphy are the measure of interest, so there is no need to visually score data. Actigraphs are less disturbing than the sensors applied for polysomnography, and it is unlikely that actigraphs substantially influence normal sleep.
Although actigraphs are an accepted measure to determine rest-activity cycles,  more subtle physiological changes cannot be detected by actigraphy. Unfortunately, the degree of standardization overall is relatively low. Different models (i.e., hardware) will give slightly different results, there are several methods to determine activity counts (time above threshold, zero crossing, digital integration),  and each company has its own algorithm to differentiate wake from sleep periods. Therefore, it is not surprising that the results of comparisons between polysomnography and actigraphy vary widely. ,,,,,, Although CNS activations and body movements often occur simultaneously, both may occur independently from each other, and thus one cannot expect a 1:1 agreement. Rather, some misclassifications are obvious: For example, someone lying awake and not moving but trying to fall asleep would be misclassified as being asleep by actigraphy. A data storage rate of 1-2 values per min is too low to be useful for an event-related analysis. , Newer devices allow for higher data storage rates (shorter sampling intervals), however, that capability usually limits the maximum duration of continuous recording.
| Electrocardiography|| |
Noise induces activations of the autonomic nervous system, like increases in blood pressure and heart rate, which can be easily measured with ECG or plethysmography. , Methods allowing for the measurement of vegetative arousals (like the ECG) are somewhat unique as they measure early stage CNS arousals (i.e., before thalamo-cortical gating, [Figure 2]) that may or may not evolve into cortical arousals of different degrees. As cortical arousals are regularly (but not always, see "Discussion" section) associated with vegetative arousals, and stronger cortical activations are associated with longer and more severe vegetative activations,  this offers a unique opportunity to measure both subtle and more obvious changes in sleep physiology with less invasive and less expensive methods than polysomnography.
Based on this idea, Basner et al. developed an ECG-based algorithm for the automatic identification of autonomic activations associated with cortical arousals.  Heart rate information of five consecutive heart beats relative to a ± 90s moving median heart rate is used to estimate the probability that the current heart beat is associated with a cortical arousal. A cardiac arousal was defined as four consecutive beats with the above mentioned probability exceeding 35% (see [Figure 4] for an example). This algorithm was later validated on extensive laboratory data from a study on the effects of aircraft noise on sleep.  EEG awakenings and cardiac arousals showed moderate to substantial agreement.  After taking spontaneous reaction probabilities into account,  exposure-response relationships between maximum sound pressure level L AS,max and EEG awakenings and between L AS,max and automatically determined ECG arousals almost ran on-top of each other.
|Figure 4: In this 1-hour period, eight aircraft noise events with maximum sound pressure levels of 65 dB(A) were played back in the laboratory with seven or eight minute intervals between playbacks. The heart rate trace is shown in the upper part of the figure, the hypnogram in the lower part. Rechtschaffen et al. sleep stages are given on the right side of the figure (W=wake, REM=rapid eye movement sleep, S1/S2/S3=sleep stages 1/2/3). Times of aircraft noise event playbacks (Noise) are given below the hypnogram as black diamonds. Times of automatic ECG activation recognition by the ECG algorithm (ECG-Act) are given above the hypnogram also as black diamonds. Adopted from Basner et al.|
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Similar to actigraphy, devices measuring the ECG are relatively inexpensive and robust. After an initial orientation, subjects can attach and detach the ECG electrodes themselves and (depending on storage capacity) can wear the device for several days and nights unsupervised (i.e., the methodological expense is low). The data are scored automatically by the algorithm described above, so there is no need to visually score data. The ECG is less disruptive than the sensors applied for polysomnography, and it is unlikely that the ECG alone substantially influences normal sleep. As shown above, repeated noise induced autonomic activations may play a key role in the genesis of hypertension and associated cardiovascular diseases, and therefore measuring autonomic activations may be an advantage from a conceptual standpoint. In the recent past, the utility of specific aspects of the ECG signal (like heart rate variability  or cardiopulmonary coupling  ) for sleep research has been acknowledged in the field. For this reason alone it will be worthwhile to sample the ECG in a field study on the effects of noise on sleep.
The method is relatively new and there are no published studies that could be used for comparison. Therefore, further validation of the ECG algorithm is desirable. This validation should investigate whether the algorithm performs equally well in every subject and in all sleep stages (the greater heart rate variability in REM sleep may pose a problem for the algorithm). Moreover, the algorithm was primarily developed for the detection of EEG arousals from sleep. However, a certain period throughout the night is spent awake, and it is unclear how to interpret heart rate increases during wakefulness (the same is true for actigraphy, see above). Basner et al.  discuss this the following way: "Situations where the subject was already awake before playback of the ANE started (10.3% of all events) were excluded from the analysis in this study […]. Comparable to actigraphy, the ECG algorithm is not able to differentiate between wake and sleep unless polysomnography is performed simultaneously. If the ECG is sampled alone, cardiac activations during wakefulness may be misinterpreted as awakenings, potentially overestimating the number of traffic noise induced awakenings. However, in situations where the subject is already awake traffic noise may nevertheless adversely affect sleep by preventing the subject from falling asleep again, and therefore prolonging spontaneous or noise induced awakenings.  In these situations, noise induced cardiac activations may indicate an increased state of arousal and, therefore, a decreased likelihood of falling asleep again. Hence, although cardiac activations during wake periods may overestimate the number of EEG awakenings, they may nevertheless be a useful indicator of noise induced sleep disturbance. Further analyses on the association of cardiac activations during wakefulness and the time needed to fall asleep again should be performed in the future".
| Signaled Awakening|| |
Several studies investigated the influence of traffic noise on signaled (or behaviorally confirmed) awakenings. , Here, the subject has to give an agreed upon signal (e.g. press a button) to indicate the awakening.
This method is very easy to use and inexpensive. Signaled awakenings are very specific (i.e., there are only few spontaneous conscious awakenings in undisturbed nights, see above).
The method has a low sensitivity, that is, relevant noise induced physiological activations will be missed. It is also not well standardized in the sense that there are no standardized instructions. These instructions may greatly influence whether or not the subject will press the response button or not. Different investigators may stress the importance of pressing the button at varying degrees, and these demand characteristics may invoke different responses. On the same note, by demanding an active cooperation of the subject the importance of the signal, reaction probability, and sleep itself may be altered. , Moreover, subjects may forget or be too tired or languid to give the signal.
| Questionnaires|| |
Typically, subjects answer questions concerning sleep quality and quantity, number and duration of nocturnal wake episodes, noise annoyance, and momentary state (e.g. tired or refreshed) after waking up in the morning.
Questionnaires represent the easiest and probably cheapest way to gather information on sleep. This method is noninvasive and unlikely to substantially alter sleep (aside from the Hawthorne effect, that is, subjects modifying an aspect of their behavior simply in response to the fact that they are being studied, inherent to all the measures described here).
The validity of assessing the effects of noise on sleep with questionnaires is at least questionable, as during most of the night the sleeper is unconscious and not aware of the surroundings, and thus the agreement between objective measures of sleep and subjective assessments of sleep quality is often poor. , This provokes false negative assessments (i.e., subjects may not be aware of relevant physiological activations that do not lead to conscious awakenings, very much like in obstructive sleep apnea). The process of falling asleep and longer wake periods during the night contribute exceptionally to subjective estimates of sleep quality and quantity, which may therefore differ substantially from objective measures.  Additionally, these subjective assessments are prone to manipulation (i.e., subjects may answer in a certain way to, for example, make a political statement or use the questionnaire as a means to express their frustration with the current noise policy). However, in a laboratory study on the effects of air, road, and rail traffic noise on sleep Basner et al,  described the following: "Although most of the night is spent in an unconscious state, on a group level, subjects were not only able to differentiate between nights with and without noise, but also between nights with low and high degrees of traffic noise exposure. Hence, if these findings extend to the field, morning questionnaires, although prone to manipulation, may be a very cost-effective way for the investigation of traffic noise effects on sleep."
| Discussion|| |
Studies employing different techniques for measuring sleep and the effects of noise on sleep cannot easily be compared. This also affects the feasibility of deriving pooled exposure-response relationships that could help inform policy and legislative processes. For future studies, it would thus be desirable to adapt a common measurement technique that would allow for comparisons of results across studies (and countries). The measurement technique should be robust and affordable (to allow for the investigation of large representative samples), yet still be meaningful and methodologically sound to allow for valid inferences.
We described five different sleep measurement techniques and discussed their advantages and disadvantages relative to studies on the effects of noise on sleep. Obviously, a "standard methodology" should be based on expert consensus, which we do not want to forejudge. However, we hope to contribute to the process of achieving expert consensus with the analyses presented here. Below, we critically discuss the joint use of actigraphy and a single channel ECG as one possible avenue for future studies on the effects of traffic noise on sleep.
Actigraphy and the ECG are robust, inexpensive, and noninvasive methods. As outlined above, potential future field studies on the effects of noise on sleep should investigate large representative subject samples (probably including risk groups). Due to the high methodological expense, this will not be possible with standard polysomnography. Already existing data from polysomnographic field studies on the effects of noise on sleep should be used to increase the level of validation of actigraphy and the ECG. The following arguments support a joint use of these two techniques:
Actigraphy is a well-established method in research on the effects of noise on sleep. It was used in studies around Heathrow,  Amsterdam,  and Cologne-Bonn Airport.  Therefore, using actigraphy ensures comparability of the results of future studies with those of the above mentioned European studies. The actigraph's event marker could be used to gather information on conscious awakenings. This, on the one hand, would ensure comparability with earlier US field studies on the effects of aircraft noise on sleep. , On the other hand, the instruction to press a button when awake would likely influence sleep itself. Higher data storage rates should be used than those commonly applied (1-2/min) in order to allow for an event-related analysis. If supported by the hardware, data storage rates of 0.5-1 Hz or higher would be desirable.
As described above, the ECG offers a unique opportunity to measure both subtle and more obvious changes in sleep physiology with less disruptive and less expensive methodology compared with polysomnography. Self-instrumentation and automatic data analysis make this an inexpensive and objective method. Nocturnal vegetative activations may play an important role in the genesis of cardiovascular disease, and therefore the analysis of heart rate information alone delivers important insights, but conclusions on the frequency of EEG awakenings also seem possible.  Obviously, one cannot expect a 1:1 agreement between EEG arousals/awakenings and cardiac activations, as there are instances of cortical arousal without cardiac arousal (e.g. arousals originating primarily from the cortex, see [Figure 3]) as well as instances of cardiac arousal without cortical arousal (e.g., due to thalamo-cortical gating, see [Figure 2]). Optimally, the ECG signal would be both sampled and stored at a high rate (if possible with at least 256 Hz , ). This would allow for offline analysis of the ECG signal, including its spectral analysis and the analysis of heart rate variability. However, this both depletes data storage and battery capacity and may restrict the maximum sampling duration. For the ECG algorithm, it would suffice if heart rate (i.e., R-R-intervals) was determined internally by the measuring device and stored at a rate of 4 Hz. Preferentially, both actigraphy and the ECG should be recorded using the same sampling clock (i.e., with the same device). This avoids data synchronization problems.
Regardless of the disadvantages described above, the subjective assessment of sleep will always be of interest not only from a scientific but also from a political and legislative point of view. The World Health Organization (WHO) started adopting the concept of Disability Adjusted Life Years (DALYs) based on the number of subjects "highly sleep disturbed" (i.e., checking of the highest 28% of the answering scale).  The percentage of subjects being highly sleep disturbed depending on noise exposure is derived from questionnaire data gathered in surveys. Moreover, despite the disadvantages described above, average subject responses accurately mirrored the degree of noise exposure in a laboratory study on the effects of traffic noise on sleep.  If this can be replicated for the field, questionnaires may be a very cost-effective way to investigate the effects of transportation noise on sleep. For these reasons, questionnaires should be a part of the methodological array assessing noise-induced sleep disturbance.
Finally, one alternative approach shall be briefly discussed here: It may be possible to gather information on cortical arousals without a full polysomnography, that is, the application of electrodes on the scalp (positions F 4 , C 4 , O 2 ), next to the eyes (EOG-L, EOG-R), above chin muscles (EMG- 1, EMG-2), and above the mastoids (M-1, M-2). One scalp electrode (e.g. F z ) together with a reference electrode (M-1) and a mass electrode may suffice to reliably detect cortical arousals. These electrodes could probably be self-administered by the subjects (although adequate skin preparation is more important for the EEG signal than for the ECG signal due to lower electrical potentials generated by the Cortex). However, even this minimal EEG montage may still disturb sleep to a certain degree. Additionally, sleep stage classification will not be possible based on this minimal montage, as the relevant standards , require additional EEG electrodes in central and occipital positions. This would, however, be less relevant if the focus of the analysis relates to awakenings.
There is one commercially available wireless system that uses proprietary dry silver-coated fabric sensors in a headband and requires no additional electrodes. A first validation study under controlled laboratory conditions in N = 26 subjects undergoing two nights of full polysomnography was recently published.  The automatic scoring algorithm of the device showed substantial chance-corrected agreement with two separate visual scorings and a visual consensus scoring, but fell behind the level of agreement of the two visual scorings (the device only differentiates wake, light sleep, deep sleep, and REM sleep). If the two visual scorers agreed on stage wake, the device agreed in 63.9%. If the device scored wake, the consensus scoring between the two visual scorers agreed in 84.8%. The authors conclude " that the wireless system shows promise as a relatively accurate system for scoring sleep." It should also be noted that because of the temporal smoothing of the algorithm, the wireless system is not suitable for scoring single epoch intervals of wakefulness or arousals in the current configuration (i.e., a noise event-related analysis would not be possible). Future development and field validation of this method will have to demonstrate its potential usefulness for investigating the effects of environmental noise on sleep.
| Conclusions|| |
The presented analyses stress the importance of a uniform methodology assessing the effects of environmental noise on sleep for future studies. This will guarantee comparability between studies and the ability to generate pooled exposure-response relationships to inform policy and legislation. We describe and discuss five different techniques for measuring sleep and the effects of noise on sleep. We also present arguments for the joint use of actigraphy and a single channel ECG as meaningful, robust, and inexpensive methods, which could be used as a starting point for an expert consensus discussion.
| Acknowledgments|| |
This manuscript is based on work performed in Partnership for AiR Transportation Noise and Emissions Reduction (PARTNER) Project 25 B (09-C-NE-PU). PARTNER is a cooperative aviation research organization, and an FAA/NASA/Transport Canada sponsored Center of Excellence. Opinions, findings, conclusions and recommendations expressed in this material are those of the authors and do not necessarily reflect the views of PARTNER sponsoring organizations.
| References|| |
|1.||Berry B, Flindell I. Estimating dose-response relationships between noise exposure and human health impacts in the UK. Report BEL 2009-001, United Kingdom, 2009. |
|2.||Basner M, Griefahn B, Berg M. Aircraft noise effects on sleep: Mechanisms, mitigation and research needs. Noise Health 2010;12:95-109. |
|3.||Basner M. Design for a US field study on the effects of aircraft noise on sleep. Report PARTNER-COE-2012-003, Cambridge, MA: Partnership for Air Transportation Noise and Emissions Reduction (PARTNER), 2012 (available at http://web.mit.edu/aeroastro/partner/reports/proj25/proj25b-final.pdf, accessed September 7, 2012). |
|4.||Oswald I, Taylor AM, Treisman M. Discriminative responses to stimulation during human sleep. Brain 1960;83:440-53. |
|5.||Davis H, Davis PA, Loomis AL, Harvey EN. Electrical reactions of the human brain to auditory stimulation during sleep. J Neurophysiol 1939;2:500-14. |
|6.||Iber C, Ancoli-Israel S, Chesson A, Quan SF. The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications. Westchester, Illinois: American Academy of Sleep Medicine; 2007. |
|7.||Rechtschaffen A, Kales A, Berger RJ, Dement WC, Jacobsen A, Johnson LC, et al. A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects. Washington, D.C.: Public Health Service, U.S. Government, Printing Office; 1968. |
|8.||Stickgold R. Sleep-dependent memory consolidation. Nature 2005;437:1272-8. |
|9.||Mignot E. Why we sleep: The temporal organization of recovery. PLoS Biol 2008;6:e106. |
|10.||Siegel JM. Clues to the functions of mammalian sleep. Nature 2005;437:1264-71. |
|11.||Wesensten NJ, Balkin TJ, Belenky G. Does sleep fragmentation impact recuperation? A review and reanalysis. J Sleep Res 1999;8:237-45. |
|12.||Bonnet MH, Doghramji K, Roehrs T, Stepanski EJ, Sheldon SH, Walters AS, et al. The scoring of arousal in sleep: Reliability, validity, and alternatives. J Clin Sleep Med 2007;3:133-45. |
|13.||Basner M, Müller U, Elmenhorst EM, Kluge G, Griefahn B. Aircraft noise effects on sleep: A systematic comparison of EEG awakenings and automatically detected cardiac activations. Physiol Meas 2008;29:1089-103. |
|14.||Basner M, Griefahn B, Müller U, Plath G, Samel A. An ECG-based algorithm for the automatic identification of autonomic activations associated with cortical arousal. Sleep 2007;30:1349-61. |
|15.||Dang-Vu TT, McKinney SM, Buxton OM, Solet JM, Ellenbogen JM. Spontaneous brain rhythms predict sleep stability in the face of noise. Curr Biol 2010;20:R626-7. |
|16.||Bonnet MH. Effect of sleep disruption on sleep, performance, and mood. Sleep 1985;8:11-9. |
|17.||Bonnet MH. Performance and sleepiness as a function of frequency and placement of sleep disruption. Psychophysiology 1986;23:263-71. |
|18.||Guilleminault C, Abad VC, Philip P, Stoohs R. The effect of CNS activation versus EEG arousal during sleep on heart rate response and daytime tests. Clin Neurophysiol 2006;117:731-9. |
|19.||Bonnet MH. Differentiating sleep continuity effects from sleep stage effects. J Sleep Res 2000;9:403-6. |
|20.||Bonnet M, Carley DW, Carskadon MA, Easton P, Guilleminault C, Harper R, et al. EEG arousals: Scoring rules and examples: A preliminary report from the Sleep Disorders Atlas Task Force of the American Sleep Disorders Association. Sleep 1992;15:173-84. |
|21.||Bonnet M, Arand DL. EEG arousal norms by age. J Clin Sleep Med 2007;3:271-4. |
|22.||Brink M, Basner M, Schierz C, Spreng M, Scheuch K, Bauer G, et al. Determining physiological reaction probabilities to noise events during sleep. Somnologie 2009;13:236-43. |
|23.||Martin SE, Wraith PK, Deary IJ, Douglas NJ. The effect of nonvisible sleep fragmentation on daytime function. Am J Respir Crit Care Med 1997;155:1596-601. |
|24.||Griefahn B, Dinisi J. Mood and Cardiovascular Functions during Noise, Related to Sensitivity, Type of Noise and Sound Pressure Level. J Sound Vib 1992;155:111-23. |
|25.||Griefahn B, Bröde P, Marks A, Basner M. Autonomic arousals related to traffic noise during sleep. Sleep 2008;31:569-77. |
|26.||Huss A, Spoerri A, Egger M, Roosli M; Swiss National Cohort Study Group. Aircraft noise, air pollution, and mortality from myocardial infarction. Epidemiology 2010;21:829-36. |
|27.||Sorensen M, Hvidberg M, Andersen ZJ, Nordsborg RB, Lillelund KG, Jakobsen J, et al. Road traffic noise and stroke: A prospective cohort study. Eur Heart J 2011;32:737-44. |
|28.||Basner M, Siebert U. Markov State Transition Models for the Prediction of Noise Induced Sleep DisturbancesMarkov-Prozesse zur Vorhersage fluglärmbedingter Schlafstörungen. Somnologie 2006;10:176-91. |
|29.||Quehl J, Basner M. Annoyance from nocturnal aircraft noise exposure: Laboratory and field-specific dose-response curves. J Env Psychol 2006;26:127-40. |
|30.||Basner M, Müller U, Elmenhorst E-M. Single and combined effects of air, road, and rail traffic noise on sleep and recuperation. Sleep 2011;34:11-23. |
|31.||Basner M, Glatz C, Griefahn B, Penzel T, Samel A. Aircraft noise: Effects on macro- and micro-structure of sleep. Sleep Med 2008;9:382-7. |
|32.||Agnew HW Jr, Webb WB, Williams RL. The first night effect: An EEG study of sleep. Psychophysiology 1966;2:263-6. |
|33.||Loredo JS, Clausen JL, Ancoli-Israel S, Dimsdale JE. Night-to-night arousal variability and interscorer reliability of arousal measurements. Sleep 1999;22:916-20. |
|34.||Drinnan MJ, Murray A, Griffiths CJ, Gibson GJ. Interobserver variability in recognizing arousal in respiratory sleep disorders. Am J Respir Crit Care Med 1998;158:358-62. |
|35.||Basner M, Griefahn B, Penzel T. Inter-rater agreement in sleep stage classification between centers with different backgrounds. Somnologie 2008;12:75-84. |
|36.||Caffarel J, Gibson GJ, Harrison JP, Griffiths CJ, Drinnan MJ. Comparison of manual sleep staging with automated neural network-based analysis in clinical practice. Med Biol Eng Comput 2006;44:105-10. |
|37.||Horne JA, Pankhurst FL, Reyner LA, Hume K, Diamond ID. A field study of sleep disturbance: Effects of aircraft noise and other factors on 5,742 nights of actimetrically monitored sleep in a large subject sample. Sleep 1994;17:146-59. |
|38.||Passchier-Vermeer W, Vos H, Steenbekkers JH, Van der Ploeg FD, Groothuis-Oudshoorn K. Sleep disturbance and aircraft noise exposure - exposure effect relationships. Netherlands: TNO, 2002 Report 2002.027. |
|39.||Brink M, Müller CH, Schierz C. Contact-free measurement of heart rate, respiration rate, and body movements during sleep. Behav Res Methods 2006;38:511-21. |
|40.||Ancoli-Israel S, Cole R, Alessi C, Chambers M, Moorcroft W, Pollak CP. The role of actigraphy in the study of sleep and circadian rhythms. Sleep 2003;26:342-92. |
|41.||Paquet J, Kawinska A, Carrier J. Wake detection capacity of actigraphy during sleep. Sleep 2007;30:1362-9. |
|42.||Hedner J, Pillar G, Pittman SD, Zou D, Grote L, White DP. A novel adaptive wrist actigraphy algorithm for sleep-wake assessment in sleep apnea patients. Sleep 2004;27:1560-6. |
|43.||Stanley N. Actigraphy in human psychopharmacology: A review. Hum Psychopharmacol 2003;18:39-49. |
|44.||Pollak CP, Tryon WW, Nagaraja H, Dzwonczyk R. How accurately does wrist actigraphy identify the states of sleep and wakefulness? Sleep 2001;24:957-65. |
|45.||Blood ML, Sack RL, Percy DC, Pen JC. A comparison of sleep detection by wrist actigraphy, behavioral response, and polysomnography. Sleep 1997;20:388-95. |
|46.||Sadeh A, Sharkey KM, Carskadon MA. Activity-based sleep-wake identification: An empirical test of methodological issues. Sleep 1994;17:201-7. |
|47.||Pitson D, Chhina N, Knijn S, van Herwaaden M, Stradling J. Changes in pulse transit time and pulse rate as markers of arousal from sleep in normal subjects. Clin Sci (Lond) 1994;87:269-73. |
|48.||Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977;33:159-74. |
|49.||Sforza E, Pichot V, Cervena K, Barthelemy JC, Roche F. Cardiac variability and heart-rate increment as a marker of sleep fragmentation in patients with a sleep disorder: A preliminary study. Sleep 2007;30:43-51. |
|50.||Thomas RJ, Mietus JE, Peng CK, Goldberger AL. An electrocardiogram-based technique to assess cardiopulmonary coupling during sleep. Sleep 2005;28:1151-61. |
|51.||Fidell S, Pearsons K, Tabachnick BG, Howe R. Effects on sleep disturbance of changes in aircraft noise near three airports. J Acoust Soc Am. 2000;107(5 Pt 1):2535-47. |
|52.||Fidell S, Pearsons K, Tabachnick BG, Howe R, Silvati L, Barber D. Field study of noise induced sleep disturbance. J Acoust Soc Am 1995;98:1025-33. |
|53.||Williams HL. Effects of noise on sleep: A review. in: Ward, E.D. (editor): Proceedings of the International Congress on Noise as a Public Health Problem, Washington, D.C., 1973. 501-11 |
|54.||Born J, Hansen K, Marshall L, Molle M, Fehm HL. Timing the end of nocturnal sleep. Nature 1999;397:29-30. |
|55.||Krystal AD, Edinger JD. Measuring sleep quality. Sleep Med 2008(9 Suppl 1):S10-7. |
|56.||Pirrera S, De Valck E, Cluydts R. Nocturnal road traffic noise: A review on its assessment and consequences on sleep and health. Environ Int 2010;36:492-8. |
|57.||Silva GE, Goodwin JL, Sherrill DL, Arnold JL, Bootzin RR, Smith T, et al. Relationship between reported and measured sleep times: The Sleep Heart Health Study (SHHS). J Clin Sleep Med 2007;3:622-30. |
|58.||Ollerhead JB, Jones CJ, Cadoux RE, Woodley A, Atkinson BJ, Horne JA, et al. Report of a Field Study of Aircraft Noise and Sleep Disturbance. London, United Kingdom: Department of Transport; 1992. |
|59.||Basner M, Isermann U, Samel A. Aircraft noise effects on sleep: Application of the results of a large polysomnographic field study. J Acoust Soc Am 2006;119:2772-84. |
|60.||Camm AJ, Malik M. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 1996;17:354-81. |
|61.||Pizzuti GP, Cifaldi S, Nolfe G. Digital sampling rate and ECG analysis. J Biomed Eng 1985;7:247-50. |
|62.||Miedema HM, Vos H. Associations between self-reported sleep disturbance and environmental noise based on reanalyses of pooled data from 24 studies. Behav Sleep Med 2007;5:1-20. |
|63.||Shambroom JR, Fabregas SE, Johnstone J. Validation of an automated wireless system to monitor sleep in healthy adults. J Sleep Res 2012;21:221-30. |
|64.||Raschke F, Fischer J. "Arousal" in sleep medicine. Somnologie 1997;2:59-64. |
Unit for Experimental Psychiatry, Division of Sleep and Chronobiology in Psychiatry, University of Pennsylvania Perelman School of Medicine, 1013 Blockley Hall, 423 Guardian Drive, Philadelphia, PA,19104-6021
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
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