DPOAE Component Estimates and Their Relationship to ... · Lauren A. Shaffer* Sumitrajit Dhar† Abstract Distortion product otoacoustic emissions (DPOAEs) recorded in the ear canal

J Am Acad Audiol 17:279–292 (2006)

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*Department of Speech Pathology and Audiology, Ball State University; †Department of Communication Sciences andDisorders, Northwestern University

Sumit Dhar, Hugh Knowles Center, Department of Communication Sciences and Disorders, 2240 North Campus Drive,Northwestern University, Evanston, IL 60208; Phone: 847-491-2470; E-mail: [email protected]

This research was supported by an NIH/NIDCD grant (R03 DC005692-01A1 to SD) and a grant from the DeafnessResearch Foundation (SD and LAS).

Part of this work was presented at the annual meeting of the American Auditory Society in Scottsdale, AZ, March 2005.

DPOAE Component Estimates and TheirRelationship to Hearing Thresholds

Lauren A. Shaffer*Sumitrajit Dhar†

Abstract

Distortion product otoacoustic emissions (DPOAEs) recorded in the ear canalare a composite or vector sum of two underlying components. The relationshipbetween hearing thresholds and DPOAE-component level, rather than compositelevel, has been of recent interest. Two different signal-processing methods,inverse fast Fourier transform (IFFT) with time-windowing and low-pass filtering,were used to obtain estimates of the levels of the two components. Componentestimates were then correlated to behavioral thresholds. Improvement in thestrength of the correlation was not significant over that of the composite.While the signal processing methods were found to yield similar estimates ofthe generation component, application of the IFFT with time-windowing methodwas more complex due to the overlap of the components in the time domain.This time domain variability was observed both within and between subjects.These results highlight the complexities of DPOAE generation and the relateddifficulties of component separation.

Key Words: Distortion product otoacoustic emissions, hearing threshold

Abbreviations: CFdp = characteristic frequency place of the distortion product;DPOAE = distortion product otoacoustic emission; FFT = fast Fourier transform,IFFT = inverse fast Fourier transform; SFOAE = stimulus frequency otoacousticemission; sgDPOAE = single generator distortion product otoacoustic emission

Sumario

Las emisiones otoacústicas por productos de distorsión (DPOAE) registradasen el canal auditivo son la resultante o la suma de vectores de dos componentessubyacentes. La relación entre el umbral auditivo y el nivel del componentede las DPOAE, más que el nivel de la resultante, ha recibido reciente interés.Se utilizaron dos métodos diferentes de procesamiento de la señal –transformaciones rápidas inversas de Fourier (IFFT) con una ventana temporal,y filtros de pasa-bajo – para estimar los niveles de los dos componentes. Losestimados de los componentes fueron luego comparados con los umbralesconductuales. La mejoría en la fortaleza de la correlación no fue significativasobre aquella de la resultante. Mientras que los métodos de procesamientode la señal mostraron estimados similares del componente generado, laaplicación de la IFFT con un método de ventana temporal fue más complejodebido al traslape de los componentes en el dominio temporal. Esta variabilidaden el dominio temporal se observó tanto entre sujetos como en relación a cada

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The idea of a “single generator”distortion product otoacoustic emission(sgDPOAE) was first proposed by

Plinkert et al (1997) when they found that theamplitude of the 2f1-f2 distortion productotoacoustic emission (DPOAE) could bealtered by introducing a suppressor tone 25Hz above the frequency of 2f1-f2. According to the authors, moderate suppressor levels of50–60 dB SPL “always clamped the DP-amplitude and extinguished the DP-gramfine structure” (Plinkert et al, 1997, p. 909).

The concept of an sgDPOAE arising fromsuppression of activity at the characteristicfrequency place of 2f1-f2 (CFdp) follows logicallyfrom a two-source model of DPOAEgeneration (Talmadge et al, 1998;Mauermann et al, 1999). Energy of theDPOAE arises due to nonlinearities at theregion of overlap of the basilar membraneexcitation patterns associated with thestimulus tones, f1 and f2. The distortionproduct energy then travels both basallytoward the ear canal and apically toward theCFdp where the basilar membrane vibratesmaximally to the frequency 2f1-f2.At the CFdp location, the distortion product energy is reflected by randominhomogeneities in the mechanics of thebasilar membrane (Zweig and Shera, 1995;Talmadge et al, 1998). The 2f1-f2 DPOAEmeasured in the ear canal is the vector sumof this reflected energy (or “reflectioncomponent”) and the basally-traveling energyfrom the overlap region (the “generationcomponent”).The fine structure pattern arisesdue to the inherently different phaseproperties of these two components, suchthat their interference creates a pattern ofamplitude variation (peaks and valleys) thatcan be seen only when the frequency-

resolution of the DPOAE recording issufficiently high (e.g., 10–50 Hz). For a morethorough discussion of fine structure andmodels of OAE generation, the reader isreferred to Talmadge et al (1998), Mauermannet al (1999), or Shaffer et al (2003). For thepurposes of this paper, we use the terms“generator” and “reflection” componentsprimarily to be consistent with our previouswork. Our intent is to distinguish the DPOAEcomponents arising from the overlap andCFdp regions, exact mechanisms operationalat these regions notwithstanding.

In light of the model described above, itis obvious that complete suppression of theCFdp location will yield a DPOAE resultingfrom only one component, the generationcomponent. This is what Plinkert et al (1997)refer to as the sgDPOAE. In the case ofcomplete suppression of the CFdp, the finestructure pattern is removed. If, however,suppression of the CFdp is not complete, thenresidual fine structure remains. Recently, wepublished a paper (Dhar and Shaffer, 2004)disputing the claim by Plinkert et al (1997)that moderate suppressor levels of 50–60 dBextinguish the fine structure pattern. Rather,we found that there is no “universal”suppressor level that will completely suppressthe fine structure pattern at all frequencies.

Talmadge et al (1999) showed that asuppressor tone yields complex effects thatdepend on the relative amplitudes of thegeneration and reflection components. Theseeffects include both increased and reduceddepth of fine structure.1 In normal-hearingsubjects the relative amplitudes of thegeneration and reflection components varyacross frequency such that in some frequencyregions the generation component may havegreater amplitude while in other frequency

sujeto particular. Estos resultados destacan la complejidad de la generaciónde DPOAE y las dificultades relacionadas con la separación de componentes.

Palabras Clave: Emisiones otoacústicas por productos de distorsión, umbralauditivo

Abreviaturas: CFdp = lugar característicos de frecuencia del producto dedistorsión; DPOAE = emisión otoacústica por producto de distorsión; FFT =transformación rápida de Fourier; IFFT = transformación rápida inversa deFourier; SFOAE = emisión otoacústica de la frecuencia del estímulo; sgDPOAE= emisión otoacústica por producto de distorsión de un generador único

DPOAE Components and Hearing Thresholds/Shaffer and Dhar

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regions the reflection component maydominate (Shaffer et al, 2003). The relativeamplitudes of the components will also varydepending on choice of stimulus parameters.When the generation component amplitudeis dominant, application of a suppressor tonewill reduce the depth of fine structure. Whenthe reflection component amplitude is larger,however, fine structure may initially deepenbecause partial suppression of the reflectioncomponent causes the relative amplitudes ofthe two components to be closer to equal.Further increasing the level of the suppressortone will ultimately reduce the contribution ofthe reflection component, thereby reducingfine-structure depth. It should also be notedthat a suppressor tone that is too high in levelmay have an additional and unwanted effectof suppressing the generation component, dueto spread of suppression into the overlap regionof the stimulus traveling waves.

Choosing a suppressor level that producescomplete suppression of the reflectioncomponent is, therefore, not a simple task.Weshowed that among 20 normal-hearing ears,suppressor tones of 45, 55, and 65 dB SPLcould cause fine structure depth to decrease,increase, or remain unchanged (Dhar andShaffer, 2004). This pattern of effects wasvariable both across frequency and betweensubjects, making the clinical application ofsuppression paradigms so complex as to limitthe viability of this approach.

The original clinical appeal of ansgDPOAE was that by removing theamplitude variability associated with finestructure, perhaps DPOAE amplitude wouldbe more robustly correlated with behavioralthresholds, thereby improving the predictionof hearing thresholds from the DP-gram.Unfortunately, this hope has not been realizedby our findings. Correlations of behavioralthresholds to the sgDPOAE were notimproved when compared to correlationsobserved in the unsuppressed condition (Dharand Shaffer, 2004).The results, however, weresomewhat ambiguous because no singlesuppressor level was able to readily yield ansgDPOAE. The question as to whether ansgDPOAE would yield better correlation withhearing thresholds, therefore, remains open.

Suppression of the reflection componentis not the only means by which to separatethe two components. Inverse fast Fouriertransform (IFFT) has also been usedsuccessfully to isolate the components (Stover

et al, 1996; Kalluri and Shera, 2001; Konrad-Martin et al, 2001). Briefly, IFFT converts ahigh resolution DP-gram into its time-domainequivalent. Time-windowing is then used toisolate the two components, which due totheir different phase properties, peak atdifferent points in time. Errors in theestimation of the two components have beenpreviously described (Kalluri and Shera,2001) and depend on how completely thecomponents separate in time and how welltime-windowing can be applied to resolvethe time differences.

Additionally, a low-pass filtering methodcan be used to isolate the components. Brownet al (1996) applied such a method to firstestimate the generator component and thento extract the DPOAE residual (reflectioncomponent) from the composite DPOAE usingvector subtraction.While they were primarilyinterested in comparing the magnitude andphase properties of stimulus frequencyotoacoustic emissions (SFOAEs) to theDPOAE residual, it is possible to use thismethod to estimate both the generation andreflection components.

In this paper, we apply the IFFT/time-windowing method, as well as the low-passfiltering method to the unsuppressed datafrom Dhar and Shaffer (2004). In pursuing thegoal of determining whether DPOAEcomponents correlate more strongly tobehavioral thresholds, we also ask how easilyseparable are the two components in normal-hearing ears, and do the IFFT/time-windowingand low-pass filtering methods yield similarestimates of the DPOAE components.

METHODS

Results from this data set have beenpublished previously (Dhar and Shaffer,

2004). With an alternate goal, the data areanalyzed with different techniques, to answerquestions pertinent to this paper.

Participants

With the approval of and following theguidelines of the Indiana University HumanSubjects Committee, pure-tone thresholdsand DPOAE recordings were obtained from20 ears of 10 normal-hearing individualsbetween 18 and 27 years of age. Participantsqualified only if they presented with hearingthresholds better than 20 dB HL (re: ANSI,


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2006) between 250 and 8000 Hz, negativehistory of otologic disease and noise exposure,and normal middle ear function indicated bya type A tympanogram, defined by staticcompliance between 0.4 and 1.5 cc and peakpressure between ±150 daPa.

Measurements

DPOAE and hearing thresholdmeasurements were made in a double-walled,sound-treated IAC booth, with the subjectcomfortably seated in a recliner. Hearingthresholds were measured in 2 dB steps usinga modified Hughson-Westlake procedure at250, 500, 1000, 2000, 3000, 4000, 6000, and8000 Hz with signals being presented throughEtymotic Research ER3 earphones from aGrason-Stadler Instruments GSI 61audiometer. Limitations related to DPOAEdata acquisition and analysis allowed us touse hearing thresholds information between1000 and 6000 Hz only, for reasons discussedin the results section.

DPOAE fine structure was recordedusing levels of 65 and 55 dB SPL for the low-(L1) and high-frequency (L2) stimulus toneswith a constant stimulus frequency ratio of1.22 between f2 frequencies of 1000 and 8000Hz. Stimulus pairs were chosen to yieldDPOAE data points separated by 0.025 mmon the basilar membrane according to theGreenwood map (Greenwood, 1990). Thecorresponding frequency resolution wasapproximately 4 and 18 Hz at DPOAEfrequencies of 1000 and 6000 Hz, respectively.For a subset of the experiments in Dhar andShaffer (2004), a suppressor tone, 25 Hz belowthe DPOAE frequency, was added at 45, 55,and 65 dB SPL. While the suppression dataare not directly presented here, some of thedata are used for comparison.

Custom-developed software was used tocontrol signal generation and recording on anApple Macintosh computer running OS X.Signals were delivered to the subjects’ earsfrom a MOTU 828 eight-channel input/outputdevice after being amplified by TDT HB6headphone amplifiers via Etymotic ResearchER2 tube phones. The output of the tubephones was delivered via the probe assemblyof a custom-built, three-port EtymoticResearch ER10B microphone. The ear-canalsignal recorded by the ER10 microphoneassembly was passed through its preamplifier(20 dB gain) and a battery-operated Stanford

Research SR560 low-noise voltage amplifierand filter. The gain provided at this stagevaried across subjects and was compensatedfor in the analysis. The signal was also band-pass filtered between 300 and 10,000 Hz atthis stage. The output of the SR560 wasdigitized by the MOTU 828 at 44100 Hz andstored on disk for analysis offline. Systemdistortion measured in a hard-walled cavity,approximately 2 cc in size, was approximately-35 dB SPL or below between the frequenciesof interest for the stimulus conditions usedin these experiments. Instrumentation usedfor both hearing threshold and DPOAEmeasurements were calibrated in a Zwislockicoupler. Voltage calibration for DPOAErecording systems does lead to variations insignal levels at the tympanic membrane(Siegel, 2002). Such variations should notaffect the inferences drawn from these data.

Data Analyses

DPOAE level, phase, and noise floorinformation was extracted from the four-second recordings of the ear-canal soundpressure using a least-squares-fit algorithm(Talmadge et al, 1999). The knownrelationship between the frequencies of thestimulus tones and the DPOAE frequencyallows construction of a sinusoidal model ofthe DPOAE for every stimulus pair.The dataare then fitted to this model to extract thelevel and phase information. Using thismethod also allows estimation of the noisefloor at the frequency of interest as opposedto using adjacent bins in the more traditionalFFT method.The initial estimates of DPOAElevel and phase were “cleaned” by eliminatingdata points where the noise floor was higherthan a preset level, which was determinedindividually for each ear. The rejectionthreshold was typically around -10 dB SPLfor the noisiest subjects. A standard cut-offwas not used, in order to preserve the sharpminima in the DPOAE-level fine structure.The “cleaned” DPOAE data were interpolatedto give a frequency spacing of 2 Hz, to complywith the demand of essentially continuousdata for the inverse FFT analysis used later.This close spacing of data points also allowedthe exact matching of DPOAE and hearing-threshold frequencies when comparisons weremade between them.


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Component Separation

Two methods, an IFFT algorithmcombined with time-windowing and moving-window averaging, were used to isolate theDPOAE components from the composite ear-canal signal. The IFFT method is commonlyused for this purpose (Stover et al, 1996;Kalluri and Shera, 2001; Dhar et al, 2002;Shaffer et al, 2003), and the procedural detailsare available elsewhere (Shaffer et al, 2003).Briefly, the frequency domain DPOAE leveland phase data are processed through anIFFT algorithm to attain a (pseudo) timedomain representation of the DPOAE,allowing separation of the early and latecomponents (generation and first reflectionpeaks, respectively) through the applicationof an appropriately sized time window. Thetime-domain representation of thesecomponents is then passed through a regularFFT transform to revert back to frequency-domain representations.The analysis is donein narrow frequency bands in an attempt tomaintain a flat phase for the generationcomponent within any given analysis window.Analyses were performed in overlappingwindows of 400 Hz with 100 Hz steps inbetween adjacent windows. Selection of thetime cut-off for separation of the componentsis critical. We chose to determine these timewindows on an individual basis for each

frequency window within each ear based onvisual inspection of the time-domain data.Wealso examined the effect of applying astandard cut-off across the entire frequencyrange. Results for such manipulation arepresented, and issues related to the choice ofan optimum cut-off are discussed.

The second method used to estimate thelevel of the generation component was adaptedafter Brown et al (1996). Based on the simpleassumption that the level of the generationcomponent varies very slowly as a function offrequency, this method involves obtaining anaverage of the ear canal DPOAE level over anarrow frequency range. Our implementationof this method involved an overlapping 50Hz averaging window with 25 Hz overlapbetween windows. Given the final frequencyspacing of 2 Hz, this would be approximatelyequivalent to the 101-point window used byBrown et al (1996). Using overlappingaveraging windows in this fashion essentiallylow-pass filters DPOAE data presumablyresulting in an estimate of the generationcomponent. A comparison of estimates of thegeneration component from the IFFT analysisand low-pass filtering for one ear from ourdata pool are presented in Figure 1.

Fine-Structure Depth

Fine-structure depth was averaged for

Figure 1. Comparison of estimates of the generator component from the IFFT and moving-window averagingparadigms for subject JP. Note the edge effects at either limit of the frequency range for the IFFT results. Inorder to avoid contamination from such effects, data points (from the IFFT analysis) over 100 Hz ranges at eitherextreme of the frequency range were omitted from any subsequent analysis.


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three fine-structure periods centered abouteach hearing-threshold frequency, with depthfor each period computed as

FSdepth = 20 * log10(Pmax/Pavg(min) ),

where Pmax is the DPOAE amplitude at amaximum and Pavg(min) is the average DPOAEamplitude of the preceding and followingminima.This initial computation was done forthe composite DPOAE data and repeated forthe estimates of the generation componentfrom the IFFT and low-pass methods.Boundaries for fine-structure periods used inthe initial analysis were used for thecomputations made on the estimates of thegeneration component.

Statistical Analyses

Available estimates of the generationand reflection components from variousanalysis techniques allowed us to makeseveral comparisons between componentlevels and hearing thresholds. Pearson’sproduct moment correlation tests wereperformed between hearing thresholds andthe (a) DPOAE level recorded in the ear canal(composite); (b) level of the generationcomponent as estimated using the IFFTanalysis (gen[I] ); (c) level of the reflectioncomponent (f2 = hearing threshold frequency)as estimated using the IFFT analysis (refl[I] );(d) level of the reflection component, when the2f1-f2 frequency is matched to the hearing-threshold frequency (reflm[i] ); (e) level of the

generation component, as estimated usingthe moving-window averaging (low-passfiltering) (gen[lp] ); and (f) estimate of thegeneration component using a suppressor at65 dB SPL (gen[s] ) from Dhar and Shaffer(2004).The SAS statistical package was usedto perform the analyses. In computingcorrelation between hearing thresholds andcomposite or component DPOAE levels inindividual ears, we make assumptions ofindependence of data obtained from two earswithin one individual, and normal distributionof these variables about each other.

RESULTS

The results are presented in three generalsections. Average data for hearing

thresholds, DPOAE levels, and DPOAE-component levels estimated using differenttechniques are presented first. Fine-structuredepth computed for the composite DPOAE-level data and the data processed through IFFTand low-pass filtering is presented next alongwith results of the correlation analyses.Generalresults of the IFFT analyses and the effect ofvariation of the time window on estimates of thegeneration-component level are presented last.

Mean hearing thresholds, DPOAEcomposite and component levels, along witherror bars representing ±1 standarddeviation, are presented in Figure 2.Averagelevel-estimates of the reflection componentare consistently lower than those of thecomposite DPOAE or the generation

Figure 2. Average hearingthresholds, DPOAE levels,and DP-component levelsestimated using varioustechniques. Error bars rep-resent ±1 standard devia-tion. Data for the suppres-sion paradigm are fromDhar and Shaffer, 2004.


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component.The only exception is observed at1000 Hz where the average level of thereflection component (when f2 = hearing-threshold frequency) is approximatelyequivalent to average level-estimates of thegeneration component. An estimate of thereflection component, where the DPOAE andhearing threshold frequencies were matched,is not available at 6000 Hz, as the requiredf2 was out of the test range. The averagelevel-estimates of the generation componentare equivalent for the three (IFFT, low-pass,suppression) analysis paradigms. Data forthe suppression paradigm are used fromDhar and Shaffer (2004). The average levelof the generation component is also equivalentto that of the composite DPOAE levelmeasured in the ear canal.The variance of thereflection component is larger than that of thegeneration component.

Average fine-structure depth measuredfrom the composite DPOAE level function andfrom estimates of the generation componentlevel obtained using the three analysismethods are presented in Figure 3. Error barsrepresent ±1 standard deviation, and the datafor the suppression paradigm are from Dharand Shaffer (2004). Mean fine-structure depthand variance are greatest for the compositeDPOAE level with some reduction observed for

the suppression paradigm.Average fine-structure depth as well as

variance is notably reduced for estimates ofthe generation component obtained usingthe IFFT and low-pass paradigms. In fact,average depth is roughly equivalent to zerosignifying absence of fine structure for bothof these analyses types. The reader isreminded that fine-structure boundaries fromthe composite DPOAE data were used tocalculate fine-structure depth from theestimates of the generation component.

Pearson’s product moment correlationvalues for hearing thresholds and variousestimates of DPOAE composite andcomponent levels are presented in Table 1.Note that a correlation of -1 is most desiredassuming that ears with higher (poorer)hearing thresholds would lead to lowerDPOAE component or composite levels. Theonly significant condition (p < 0.01) isobserved for correlation between hearingthresholds and composite DPOAE level for1000 Hz. In general, no consistent pattern ofcorrelation between hearing threshold andcomposite or component DPOAE levelemerges.

As an intermediate step to obtainingestimates of DPOAE component levels, themagnitude and phase of the DPOAE signal

Figure 3. Average fine-structure depth computed from raw DPOAE data and from component estimates usingvarious separation techniques. Error bars represent ±1 standard deviation.


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recorded in the ear canal is processed throughan IFFT algorithm. The output of this stageof the analysis process, from every ear in thedata pool, for one frequency window (2600-3000 Hz) is displayed in Figure 4. Subjectnumber and the contributing ear aredisplayed in each panel. The frequency

window was chosen randomly without any apriori knowledge of the variety of responsesobserved. Results in panels 4/R and 8/R couldbe considered typical of a two-source model,where the two main “lobes” of energy arerepresentative of the generation (early) andreflection (late) components. In panels 4/R and

Table 1. Pearson’s Product Moment Correlation Computed between Hearing Thresholds and DifferentMeasures of DPOAE Composite or Component Levels

1000 2000 3000 4000 6000

Composite -0.5936* -0.39941 -0.48726 0.31969 -0.03405

Gen[I] -0.44806 -0.45479 -0.37094 0.05192 0.32716

Gen[LP] -0.48254 -0.42032 -0.37761 0.15601 0.3822

Gen[s] -0.31182 0.33084 -0.5632 0.19372 -0.33753

Refl[I] -0.45951 -0.00498 0.02267 0.48242 0.29403

Reflm[I] -0.55634 -0.30913 -0.01742 0.14186

* p < 0.01Note: “Composite” represents the DPOAE recorded in the ear canal. “Gen[I,LP,S]” represents estimates of the generator component

using the IFFT, low-pass, and suppression paradigms. The suppression data are from Dhar and Shaffer (2004). “Refl[I]” and “Reflm[I]”represent estimates of the reflection component with f2 and 2f1-f2 matched to the threshold frequency, respectively.

Figure 4. Results of IFFT analyses on data from individual ears for frequencies between 2600 and 3000 Hzfor 20 ears. The subject number (0 to 9) and the ear (L/R) are marked in each panel. Each trace is plotted on atime axis (abscissa) between -5 and 40 msec, and a level axis (ordinate) between -50 and -10 dB SPL.


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8/R, the clear separation between the twocomponents allows for ready selection of theupper limit of the time window to separatethe generation component. However, greatvariety is observed in the morphology of thetime-domain response across individuals andbetween ears within individuals. Thevariability and ambiguity in the “cut-off ”between the two components is noticeableacross ears. This variability, even within onefrequency window, motivated us to custom-select the cut-off for each ear withinindividual frequency windows. The peak-level of the generation component shows lessvariability across ears as compared to thepeak-level of the reflection component. Inthe extreme, the reflection component is very

small or absent in some cases (e.g., 0/L, 6/R,7/R). The results of using a standard cut-offare presented below with the consequencesexamined in the “Discussion” section.

The effects of variations in the upperlimit of the time window used to isolate thegeneration component in two ears arepresented in Figures 5 and 6. The lightertrace in each panel represents the compositeDP-level function with the darker tracerepresenting an estimate of the generationcomponent with a certain cut-off. Estimatesof the generation component withincreasingly shorter time windows aredisplayed for 8, 6, 4, and 2 msec windows inthe panels from the top to the bottom of thefigures. The bottom-most panel displays anadditional trace in a dashed line. This is theestimate of the generation componentobtained with custom-selected windows.These data were used in our estimation offine-structure depth and correlation analysis.Elimination of fine structure spreads towardthe higher frequencies with the shortening of

Figure 5. Effects of time-window variation on esti-mates of the generator component from one subject(KG). Each panel, except the bottom-most, displaystwo traces. The light trace, used as reference, repre-sents the composite DPOAE level. The darker tracein each panel represents an estimate of the genera-tor component computed using a specific-durationtime window. Decreasing window-lengths of 8, 6, 4,and 2 msec were used to generate the data in the pan-els from the top to the bottom of the figure. The bot-tom-most panel has an additional trace (dashed line)representing the estimate of the generator componentfrom custom-selected time windows. In this process,we examined the time-domain output of the IFFTanalysis in each frequency window and individuallyselected the upper limit of the time window. Therange of the ordinate is constant across panels, butthe minimum and maximum vary.

Figure 6. Effects of time-window variation onestimates of the generator component from anothersubject (JP). The format of this figure is similar toFigure 5.


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the time window in both Figures 5 and 6.Fine-structure spacing appears to broadenbefore disappearance as the time window isshortened as well. A reduction in the overalllevel of the DPOAE is observed when a 2msec time window is used. Some “structure,”albeit broadly spaced, remains even for theshortest time window or the custom-selectedtime window. At the very lowest frequencies,some structure reappears for the 2 msec timewindow.

DISCUSSION

Fine structure has been identified as apotential limitation to the diagnostic

utility of DPOAEs (Shaffer et al, 2003; Dharand Shaffer, 2004; Shera, 2004). Theargument that the natural variation inDPOAE level, as a result of the interactionbetween two DPOAE components, limits thecorrelation between DPOAE level andhearing thresholds is intuitively appealing.The problem is perhaps made worse by thepresence of threshold microstructure, whichadds another source of variability. A possiblesolution would be to isolate the DPOAEcomponents from the overlap and CFdp

regions and use the level of the appropriatecomponent as a predictor of hearing status ina certain region of the cochlea. It is with thisbackdrop and interest in the complexities ofisolating DPOAE components from normal-hearing human ears that we present ourcurrent work.

In the recent past we have attempted toisolate the DPOAE components using auniversal suppressor tone close to the DPOAEfrequency (Dhar and Shaffer, 2004). However,our results questioned the viability of such aprotocol as we found great variation in theeffect of a (fixed-level) suppressor tone acrossears and across frequency in a given ear. Thepossibility exists that the exploration of agreater stimulus-suppressor-parametermatrix could lead to the identification of astimulus-suppressor combination resulting inbetter isolation of the generation component.The ideal solution would be the identificationof a stimulus condition that biases the earcanal DPOAE signal heavily toward thegeneration component.A low-level suppressortone could then be used to eliminate thecomponent from the CFdp region withoutadversely affecting the generation component.The stimulus space for DPOAE recordings is

rather complex, as variables include stimulus-frequency ratio as well as relative stimuluslevels. The stimulus-frequency ratio of 1.22,routinely used in clinical protocols, appearsto serve the purpose of biasing the ear canalDPOAE signal toward the generationcomponent. A greater bias could perhaps beachieved by using a ratio of 1.26 but at thecost of lowered overall level, thereby adverselyaffecting signal-to-noise ratio (Dhar et al,2005). Published data, however limited, alsoindicate that the composite DPOAE signal inthe ear canal has the greatest bias toward thegeneration component at higher stimuluslevels (e.g., Konrad-Martin et al, 2001; Dhar,et al, 2005). Unfortunately, there also isevidence that the sensitivity of DPOAEs tophysiological changes in the hearingmechanism diminishes with increasingstimulus levels (e.g., Whitehead et al, 1992).Thus, finding the ideal suppressor appears tobe a complex problem.

Another method, popularly used inisolation of DPOAE components, is the IFFTand time-window combination (Stover et al,1999; Kalluri and Shera, 2001; Knight andKemp, 2001; Konrad-Martin et al, 2001; Dharet al, 2005). In taking this approach, we askedtwo questions, (1) How separable are the twoDPOAE components in normal-hearinghuman ears? and (2) Is the single-componentDPOAE level a better predictor of hearingstatus?

Correlation

In our data, correlation between hearingthresholds and DPOAE level does not showany improvement when DPOAE componentrather than composite level is used in theanalysis.This remains true irrespective of theDPOAE component used in the comparison,namely the generation component estimatedusing the IFFT method or the low-passfiltering method, or the reflection component,whether or not the DP-frequency is matchedto the threshold-frequency.We recognize thatthe small N of this study, as well as theexclusive inclusion of normal-hearing ears,may have limited the extent of theimprovement in the magnitude of correlation.

Component Separation

Separation of DPOAE components byIFFT and time-windowing is a three-step


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procedure. In the first, the frequency-domainDPOAE level and phase data are passedthrough an inverse FFT algorithm, whichresults in a time-domain representation of theDPOAE signal. An idealized schematic of theresults of this step is presented in Figure7(a). The two components are representedas independent “lobes” of energy clearlyseparated in time, with the early and latelobes associated with the generation andreflection components, respectively. Success

in separating the two components is integrallytied to the temporal separation between thecomponents at this stage. The rectangularrepresentation of the time window capturesthe entire generation component withoutincluding the reflection component.

Examination of Figure 4 shows that suchclear separation between the two componentsis rare at best, even when data from the samefrequency range are considered from differentears. The variable that largely controls theresults of the IFFT analysis and causesdeviation from the idealized result shown inFigure 7(a) is the slope of the phase of theDPOAE components. The time axis in theresults of any IFFT analysis represents ameasure of the group delay of each DPOAEcomponent. Group delay is defined as thenegative of the slope of the phase versusfrequency function. Thus, the generationcomponent can be centered on the zero ofthe time axis if the phase of that componentis constant as a function of frequency.Theoretically, constant phase results only ifDPOAEs are recorded with a fixed stimulusfrequency ratio (f2/f1), and if an assumptionof cochlear scaling symmetry can be made.Practically, these conditions are satisfiedrelatively easily by using a fixed-ratioparadigm and by performing the IFFT insmall frequency-ranges over which scalingsymmetry is a reasonable assumption.

The slope of the phase of the reflectioncomponent also determines its position alongthe abscissa, with a linear phase-frequencyrelationship resulting in a well-defined lobeof energy. The reflection component has beenshown to be equivalent to stimulus frequencyOAEs (SFOAEs) recorded under comparablestimulus conditions (Kalluri and Shera, 2001).SFOAE group delays have been demonstratedto be inversely proportional to frequency.More importantly, for our purposes,considerable variability has beendemonstrated in SFOAE group delays (Sheraand Guinan, 2003) and other reflection source(place fixed) emissions (Goodman et al, 2003).This variability in group delay has thepotential of causing overlap between the twocomponents (Figure 7b-d), making themharder to separate.

Choosing a Time Window

The length of the time window used toisolate the generation component determines

Figure 7. Schematic of possible results of IFFT pro-cedure and the consequence of time-windowing. Theabscissa represents a measure of time as derivedfrom the phase slope of frequency-domain DPOAEdata. The early and late “lobes” of energy representthe generator and reflection components, respec-tively.The reflection component is filled while the gen-erator component is not. The rectangular box over-laid across the panels represents the time windowused to isolate the generator component.


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the accuracy of the separation process. Atime window of appropriate duration isexpected to “capture” the entire generationcomponent with minimal contamination fromthe reflection component.This, in turn, shouldsignificantly reduce or eliminate finestructure without causing a decrease in theoverall DPOAE level. These conditions areperhaps best approximated by the 4 msecwindow in Figures 5 and 6. Too short a timewindow (Figure 7b) captures only part of thegeneration component, thereby resulting ina reduction in the overall DPOAE level. Onthe other hand, too long a time window causesthe estimate of the generation component tobe contaminated by part of the reflectioncomponent (Figure 7c).

Kalluri and Shera (2001) treat the issueof the appropriate time window in somedetail. Their approach is to use a windowduration tied to the estimated group delay ofan SFOAE at a given frequency. Based on ameasured 35% variability about an estimatedSFOAE group delay of 15 periods of thestimulus frequency, Kalluri and Shera (2001)use a nine-period long window to isolate thegeneration component. The use of a period-based window results in a frequency-dependent window duration.The justificationfor a window duration that varies withfrequency is evident in Figures 5 and 6, wherefine structure is eliminated at the lowestfrequencies for the longest time windows.The “corner frequency” of fine-structureelimination moves toward higher frequenciesas the duration of the time window is reduced.This observation is consistent with the inverseproportionality of SFOAE group delay withfrequency. Curiously, some fine structurereturns at the very lowest frequencies forthe 2 msec window.

In our application we customize theselection of window duration further byvisually examining the IFFT results for eachfrequency range in each ear and choosingthe best possible window duration. Kalluriand Shera’s (2001) application of the nine-period window is efficient in avoidingcontamination of the generation componentby the reflection component. However, this isachieved at the potential cost of not capturingthe entire generation component. This wouldresult in an undesirable reduction in theoverall DPOAE level due to a failure to capturethe entire generation component (Figure 7b).

The duration of the time window emerges

as the most important variable in this processof separating DPOAE components.Unfortunately, the choice of the optimumwindow duration also emerges as a complexproblem to solve. While our protocol ofindividually choosing the window duration insmall frequency ranges in each ear attemptsto overcome the problems associated withthe variability in group delays of eithercomponent, it does little to resolve the othersource of complexity—overlap between thetwo DPOAE components. This appears to bea common occurrence in Figure 4.

Let us assume, for this discussion, thatwe are able to discern the two componentsseparately even when there is overlapbetween them—much like in Figure 7b–d.One of three choices in window duration canbe made under such circumstances. First,we could choose a window narrow enough toavoid the reflection component (Figure 7b).This would successfully eliminate finestructure but underestimate the generationcomponent. Second, a window wide enough toencompass the entire generation componentcould be chosen (Figure 7c). Such a windowwould allow the earliest parts of the reflectioncomponent to be included in the estimate. Inturn we would see residual fine structure inthe estimate of the generation component.Theperiodicity of the fine structure would bebroader (wider frequency spacing of adjacentmaxima or minima) as the reflectioncomponent causing the interference patternwould have a short delay. This indeed isobserved in Figures 5 and 6, with finestructure depth increasing and frequency-spacing decreasing with increasing windowduration. Finally, we could choose a timewindow that is too narrow to encompass theentire generation component but wide enoughto include parts of the reflection component(Figure 7d). We would expect residual finestructure with a reduction in the overallDPOAE level, in this case. While Kalluri andShera (2001) have demonstrated the firsttwo types of errors, they deemed the thirdpossibility hypothetical.We demonstrate thistype of error in both Figures 5 and 6, againhighlighting the preponderance of caseswhere there is significant overlap between thetwo components.

In the second scenario above, we arguedthat some fine structure, albeit broadlyspaced, appears in the generator componentdue to the inclusion of the “earliest” parts of


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the reflection component in its estimate.Recall that we computed fine-structure depthfor the generator component using fine-structure boundaries from the compositeDPOAE data.Thus, such broad fine structurewas not accounted for, and our estimate offine-structure depth is biased from that pointof view. While it would be easy to adjust thefrequency limits of each fine-structure periodwhen evaluating the level function of thegenerator component, doing so would neglectthe possibility that some variation in thelevel of the generator component could bearising from sources other than interferencewith the reflection component—changes incochlear nonlinearity, as a function offrequency, could be one such source.

As a parallel process, we analyzed thecomposite DPOAE level data using a moving-window average, in effect low-pass filteringthe composite data to arrive at an estimateof the generation component. In thesubsequent correlation analysis, these dataperformed no worse than the data processedthrough the significantly more complex andinvolved IFFT algorithm. These preliminaryresults along with our observation of theprevalent overlap between DPOAEcomponents force us to question the potencyof the IFFT process, even when applied to acomparatively small population.

We did not estimate the reflectioncomponent by vector subtracting thegenerator component (estimated using low-pass filtering) from the composite DPOAEdata. Such an estimate of the reflectioncomponent would include multiple reflectionsfrom the CFdp region following internalreflections of both the generator and initialreflection components from the middle earboundary (Dhar et al, 2002). Because wewere interested in estimating the energyspecifically from the overlap and CFdp regions,the inclusion of multiple reflections wouldcontaminate the estimate of the reflectioncomponent. To deal with this issue, only thefirst reflection “lobe” was isolated in theIFFT/time-windowing analysis; therefore thereflection component estimates that we reportcorrespond to the first reflection from theCFdp region.

CONCLUSION

In closing, we return to the clinicalmotivation for separating DPOAE

components. In light of the complexities incomponent isolation, either through thesuppression method or the IFFT method, isthis a goal worth pursuing? The motivationto isolate the components comes from thetheory that fine-structure level variation ispartly responsible for the overlap in DPOAE-level distributions of normal-hearing andhearing-impaired populations (Gorga et al,1997). Any method that could reduce finestructure might aid in separating the twopopulations. Objectively, DPOAEs have beenrepeatedly demonstrated in animal models tobe extremely sensitive to physiologicalchanges in the cochlea (e.g., Davis et al, 2004;Davis et al, 2005). In this area attempts havebeen made to relate DPOAE level withpermanent threshold shifts (PTS—asmeasured through evoked potentials) andcochleograms (to measure hair cell loss). Theconclusion often is that DPOAEs are moresensitive than PTS measures as they arereflective of morphological changes in outerhair cell populations shy of complete haircell loss. Experiments in these animal modelsare “cleaner” as (relatively) controlled lesionscan be introduced and their effects examinedthrough direct (e.g., cochleograms) andindirect (e.g., DPOAEs ) means. Thus,isolating DPOAE components would appearto hold clinical promise.

As applied to human ears with hearingloss, we are at a disadvantage due to unclearetiologies and overlapping pathologies thatare commonplace. A behavioral measure ofhearing sensitivity in humans may very wellencompass a physiological domain greaterthan that involved in DPOAE generation.This questions the validity of attempting tocorrelate behavioral hearing thresholds withany measure of DPOAEs. Indeed there isevidence that individuals classified as “normalhearing” according to their behavioralaudiogram may demonstrate lowered DPOAElevels (Zhao and Stephens, 2000).Interestingly, these individuals oftendemonstrate deficit in other areas of auditoryperformance, such as speech discriminationin noise. This finding once again suggeststhat whether or not DPOAE measures canever be used to predict hearing thresholds,DPOAEs still hold promise as a measurethat may be more sensitive than theaudiogram for certain cochlear pathologies.


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Acknowledgments. We thank Ms. Angela Thomsenfor her help with data analyses. Thanks also to twoanonymous reviewers for constructive and expedi-tious reviews.

NOTE

1. Depth is calculated as the ratio of the amplitudeof a fine-structure maximum (peak) to the amplitudeof the adjacent minima (valleys). The greatest depthof fine structure results when the two components areapproximately equal in amplitude such that when thecomponents are in phase they sum creating anamplitude maximum, and when they are 180 degreesout of phase, they cancel, creating an amplitudeminimum.

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DPOAE Component Estimates and Their Relationship to ... · Lauren A. Shaffer* Sumitrajit Dhar† Abstract Distortion product otoacoustic emissions (DPOAEs) recorded in the ear canal