Synchronized spontaneous otoacoustic emissions analyzed in a time-frequency domain

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Synchronized spontaneous otoacoustic emissions analyzed in atime-frequency domain

W. Wiktor JedrzejczakInstitute of Physiology and Pathology of Hearing, ul. Zgrupowania AK “Kampinos” 1, 01-943 Warszawa,Poland

Katarzyna J. BlinowskaDepartment of Biomedical Physics, Institute of Experimental Physics, Warsaw University, ul. Hoza 69,00-681 Warszawa, Poland

Krzysztof Kochanek and Henryk SkarzynskiInstitute of Physiology and Pathology of Hearing, ul. Zgrupowania AK “Kampinos” 1, 01-943Warszawa, Poland

�Received 6 August 2008; revised 22 September 2008; accepted 23 September 2008�

A synchronized spontaneous otoacoustic emission paradigm was used to measure the response intime intervals of 80 ms following a click stimulus. The responses obtained were decomposed intobasic waveforms by means of adaptive approximations using a matching pursuit algorithm.High-resolution time-frequency distributions of signal energy were calculated and showed threetypes of component: �1� purely evoked of duration less than 5 ms, �2� longer lasting and decaying,with exponentially decreasing amplitude, and �3� long lasting and stable. The distributions of thefrequencies of components of different durations were similar, with most components falling withinthe 1–2 kHz interval. It is shown that the presence of long-lasting components may influence theestimation of the latency of evoked emissions, especially at higher frequencies where the evokedpart has a very short duration. © 2008 Acoustical Society of America. �DOI: 10.1121/1.2999556�

PACS number�s�: 43.64.Jb, 43.60.Hj �BLM� Pages: 3720–3729

I. INTRODUCTION

Studies of otoacoustic emissions �OAEs� are essentialfor gaining a better understanding of the mechanisms of thephysiology of hearing and are also important for the diagno-sis of hearing impairment. OAE signals �reviewed by Probstet al., 1991� can be generated by the cochlea spontaneouslyor following external stimulation. OAEs evoked in this waycan be produced by a broadband signal �transient evokedotoacoustic emissions �TEOAEs�� or by tonal stimuli �stimu-lus frequency otoacoustic emissions�, or by a combination oftonal stimuli �distortion product OAEs �DPOAEs��. Sponta-neous OAEs �SOAEs� arise without external stimuli and arecharacterized by a stability of amplitude in time and a nar-row bandwidth. They are measured by averaging the spectraof signals recorded in the ear canal. Nevertheless, the mostcommon method of recording SOAEs is by synchronizingthem with broadband click stimuli. The paradigm is similarto that used to record TEOAEs, but the acquisition windowis extended from 20 to 80 ms. In this way, synchronizedSOAEs �SSOAEs� arise, which are long-lasting OAEs elic-ited by the broadband stimulus. In general, SSOAEs are con-sidered to represent SOAEs �e.g., Wable and Collet, 1994;Prieve and Falter, 1995�. However, in the case of SSOAEmeasurement two main kinds of activity may be determined:�1� the components of measurable equilibrium amplitude and�2� the components of characteristic time of exponential de-cay �e.g., Sisto and Moleti, 1999�.

The typical frequency of SOAEs ranges from
0.5 to 6 kHz �Probst et al., 1991� with most appearing in the
3720 J. Acoust. Soc. Am. 124 �6�, December 2008 0001-4966/2008/12

ution subject to ASA license or copyright; see http://acousticalsociety.org/c

range 1–2 kHz. Despite reports of frequency shifts ofSOAEs in certain conditions �e.g., Penner et al., 1994; Brie-nesse et al., 1997� and cases of appearing and disappearingSOAEs �e.g., Burns et al., 1994; Smurzynski and Probst,1998�, generally they show only small changes of frequencyand amplitude �e.g., Lind and Randa, 1992; van Dijk et al.,1994; Frick and Matthies, 1998�. This stability suggests thatSOAEs are generated at specific sites distributed along thebasilar membrane and that it is very likely that some struc-turally unique feature of the organ of Corti is involved intheir generation �Probst et al., 1991�.

The overall findings of different authors �e.g., Talmadgeet al., 1993; Penner and Zhang, 1997� have indicated thatSOAEs can be detected in about 70% of individuals withnormal hearing and that they occur more frequently in fe-males than in males. The mean number of SOAEs generatedper ear also tends to be higher in females than in males andSOAEs appear to be detected significantly more frequentlyin right ears than in left ears. Furthermore, most studies haveindicated a higher incidence of SOAEs in young children; forexample, Morlet et al. �1995� reported an 84% occurrence ofSOAEs in babies in the first 30–40 weeks of life.

The influence of the occurrence of SOAEs on evokedemissions was also investigated for TEAOEs �e.g., Kulawiecand Orlando, 1995; Osterhammel et al., 1996� and forDPOAEs �e.g., Ozturan and Oysu, 1999; Kuroda et al.,2001�. These studies showed that the evoked responses ob-tained from ears with SOAEs had significantly higher levels
than those obtained from ears without SOAEs. This differ-
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ontent/terms. Download to IP: 129.21.35.191 On: Mon, 22 Dec 2014 07:03:52

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ence was present in a range of different age groups, fromnewborn babies to adults �e.g., Prieve and Falter, 1995;Prieve et al., 1997�.

It is well known that SOAEs are often visible as narrowpeaks within TEOAE spectra �e.g., Probst et al., 1986�. Theirpresence may be demonstrated even more clearly using time-frequency �TF� distributions that show the energy of theevoked emissions �e.g., Konrad-Martin and Keefe, 2003�,where they form a continuous trace of energy that spans thewhole length of the signal. Similar structures were observedin TF distributions of TEOAE obtained by Jedrzejczak et al.�2004�, in which the distributions consisted of short-livedcomponents that obeyed the latency-frequency power lawand long-lasting components of very narrow bandwidth. Thehigh incidence of long-lasting components was observed inthe TF maps of neonates �Jedrzejczak et al., 2007�. The cor-respondence of these components with SOAE spectra wasreported using a sample of subjects. The increased latenciesin preterm neonates in comparison with full-term neonateswere explained by the longer latencies of these long-lastingcomponents.

There is evidence that the cochlear sites associated withthe SOAE frequencies have the highest amplification by theouter hair feedback system and that the SOAE frequenciescorrespond to minima in the hearing threshold microstructure�Schloth, 1983�. Sisto et al. �2001� showed that an analogous

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FIG. 2. Comparison of TF distributions of energy for 20 ms �top� and 80 m
spectrum of SSOAEs �signal from 20 to 80 ms�. The spectra were rotated to mat
J. Acoust. Soc. Am., Vol. 124, No. 6, December 2008 Jedrzejczak et al.:


relationship exists between the presence of SSOAEs �andmore generally, long-lasting OAE presence� and cochlearfunctionality within a specific frequency range. This propertyshows promise as a means of monitoring hearing defectsearly and highlights the possibility of localizing sites of co-chlear damage. Hence, the properties of SSOAEs and theirconnection with TEOAEs warrant further investigation.

In the study reported herein, high-resolution time-frequency analysis based on matching pursuit approach isproposed, in order to further investigate SSOAEs and theproperties of their decaying and stable components.

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Analysis of synchronized spontaneous otoacoustic emissions 3721


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II. EXPERIMENTAL PROCEDURE

The OAEs from 46 young females �20–35 years� weremeasured in low ambient noise conditions using an ILO-88apparatus �Otodynamics LTD, London� running softwareversion 5. All subjects were laryngologically healthy withoutany otoscopic ear abnormalities. Impedance audiometry testsrevealed normal type A tympanograms and normal acousticreflexes. In pure-tone audiometry, hearing thresholds werebetter than 20 dB HL for all test frequencies �0.25, 0.5, 1, 2,3, 4, 6, and 8 kHz�.

Standard click stimuli �average amplitude −80�2 peakdB SPL, nonlinear averaging protocol, described by Kemp etal., 1986� were used to evoke 480 TEOAE responses. TheSSOAE spectra were also measured using the default ILOprotocol. In accordance with this, the OAEs evoked by clickstimuli of 80 dB SPL were recorded in an 80 ms window.The first 20 ms of each averaged response was discarded andthe responses from the latter 60 ms were considered to beSSOAEs. In addition, 80 ms waveforms were collected intwo consecutive recordings, each consisting of 240 singletrials. This number was the maximum number of trials oflength 80 ms that could be stored by the ILO system. In thisway, 480 single responses were collected for subsequent off-line analysis. Recordings were divided into two sets and twoaverages A and B were calculated. The quantities �A+B� /2and �A−B� /2 were used as signal and noise estimates, re-spectively. All responses were high-pass filtered at 700 Hz toavoid low-frequency noise.

III. METHOD

The method of adaptive approximations using thematching pursuit �MP� algorithm was introduced by Mallatand Zhang �1993� and first applied to physiological signalsby Blinowska and Durka �1994� and to OAE analysis byJedrzejczak et al. �2004�. The approach is based on adaptivedecomposition of the signal into waveforms �also called at-oms� from a large and redundant set of functions �calleddictionary�. This set of functions is much more extensivethan the repertoire of functions used in fast Fourier transform�FFT, sinusoid waves only� or wavelet transform �particularfunctions bound by an inverse relationship between fre-quency and time span�. Using these latter techniques resultsin a lower TF resolution �Jedrzejczak et al., 2004�. The ad-vantage of the MP algorithm over the traditional wavelettransform is that the former is fully adaptive and does notinvolve a division of the data into frequency bands. A fulldescription of this method and a comparison between it andother methods of TF analysis, based on simulation studiesinvolving gammatones, can be found in Jedrzejczak et al.,2004 and Jedrzejczak et al., 2006.

In the MP procedure, waveforms are analyzed using aniterative procedure, starting from the waveform that gives thehighest value when multiplied by the signal, thus accountingfor the largest contribution to the signal energy. The subse-quent waveforms are fitted to the residual energy value ob-tained after each iteration. The procedure may be stoppedafter the desired percentage of energy has been accounted for
or after a given number of iterations �in other words, after
3722 J. Acoust. Soc. Am., Vol. 124, No. 6, December 2008 Jedrzejczak


certain number of atoms describing the signal were found�. Ithas been shown �Jedrzejczak et al., 2006� that TEOAE re-sponses from healthy adults can be described efficiently by afew �in average 7–10� waveforms �resonance modes�, whichare characteristic not only for each subject but also for eachear. However, in this case instead of “number of atoms” rule,we use in some degree equivalent criterion of 80% of energyaccounted for. In this way, only the substantial part of thesignal, i.e., which is not influenced by noise, is subjected tofurther analysis.

A stochastic dictionary �e.g., Durka et al., 2001� of Ga-bor functions �sine-modulated Gaussian functions� was used,because these functions provide the highest TF resolution�Mallat, 1999�. The size of this dictionary was 106 wave-forms for 20 ms signals and 4�106 waveforms for 80 mssignals. The Gabor function is expressed by the followingformula:

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FIG. 3. Histograms of time spans of the OAE components in three octavebands centered at 1, 2, and 4 kHz and cumulative histogram for all fre-quency bands for 80 ms signals. Note that the vertical scale differs between
histograms �for sake of pictures clarity�.
et al.: Analysis of synchronized spontaneous otoacoustic emissions


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g��t� = K��,��e−��t − u�/s�2cos��t − u� + �� , �1�

where amplitude K is chosen such that �g��=1, s��0� is thetime span, � is the frequency modulation, u is the latency,and � is the phase.

The use of Eq. �1� permits the description of waveformsby physically meaningful parameters defined as follows: �i�the amplitude is determined as the maximum of the modulusof the waveform at a certain frequency; �ii� the latency ismeasured as the time taken from onset of the stimulus to themaximum point in the waveform envelope; and �iii� the time-span parameter is defined as half the width of the waveformenvelope and can be interpreted as the duration of the wave-form �Fig. 1�.

By using the MP algorithm, a set of sorted waveforms isobtained, ranked in terms of energy. The TF characteristic iscalculated as the superposition of the energies of the wave-forms thus obtained. The TF distribution is free of crossterms, which are a cause of blurring in the TF maps that areobtained using the Wigner–Ville, Choi–Williams, or othersimilar approaches �e.g., Jedrzejczak et al., 2004�. The rela-tionship between the waveforms determined by the MP pro-cedure and the resonant modes of TEOAEs was established

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FIG. 4. The left-hand panel shows the TF map of the 80 ms OAE signal. TheSOAE. �b� Ear with multiple SOAEs. �c� Ear without SOAEs but with loncomponents found by MP analysis are marked on the SSOAE spectra.

in Blinowska et al. �2007�.



For all the parameters investigated, the statistical signifi-cance of the differences between the datasets was evaluatedusing the Wilcoxon rank sum test. This is an equivalent ofthe student’s t-test and is generally used when the popula-tions that are analyzed do not have normal distributions. As acriterion of significance, the 95% confidence level �p�5�10−2� was chosen.

The goodness of fit of the curves was estimated bymeans of R2. This is defined as the ratio of the sum ofsquares of the regression and the total sum of squares. R2 cantake any value between 0 and 1, with a value closer to 1indicating a better fit.

IV. RESULTS

Figure 2 shows an example of TF distributions of OAEenergy for recordings with 20 and 80 ms intervals betweenstimuli. The maps for the first 20 ms are quite similar. Thesmall differences that arise are probably �i� due to the factthat the 20 ms recordings were registered using a nonlinearprotocol �standard TEOAE acquisition mode� and 80 ms re-cordings using a linear one �standard SSOAE acquisitionmode�, and �ii� due to fluctuations in the response between

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is particularly visible in the first 5 ms of the distributions,where additional structures are evident on the 80 ms map.The difference is connected with the “ringing” phenomenonaffecting the first few milliseconds of linear response. Wheninspecting the lower figure, two general kinds of componentmay be observed: those of short time span that occur in thefirst 20 ms and those that are present during the whole 80 msrecording. The latter long-lasting components are character-ized by a very narrow bandwidth.

Histograms of the time spans of the waveforms for allsubjects are shown in Fig. 3. The data are presented in octavebands with center frequencies of 1, 2, and 4 kHz and forwhole frequency range. In all frequency bands, most compo-nents have time spans between 0 and 5 ms. However, forhigher frequencies there are more components with lowertime spans. In all plots, there is a group of very long com-ponents of time spans greater than 40 ms. The cumulativeplot for whole frequency range is bimodal with second maxi-mum between 60 and 70 ms. This value is influencedstrongly by the length of acquisition window. Some long-lasting waveforms may continue outside of the measurementwindow �as suggested by TF plots�, the values of their timespans being determined by the window length. In fact, theirtime span should be defined as “greater than.” About 60% ofthe components have a time span of 0–5 ms. These compo-nents can be classified as “purely” evoked. Stable long-lasting components are defined as those that have a time spanlonger than 40 ms. The components from the 5–40 ms rangedo not fall in any of the above categories; they are definedhere as longer-lasting, decaying components. An additionalcriterion for the identification of long-lasting componentswas their amplitude. Waveforms that had an amplitudehigher than the noise level in a given frequency were ana-lyzed further, using estimates from the spectrum of half thedifference between buffers A and B, i.e., �A−B� /2. The de-caying components in high-frequency band are well sepa-rated from transient evoked responses. For lower frequencybands, only small drop in histogram around 5 ms testifies fortheir identity. However, in time-frequency distributions�Figs. 2 and 4�, they can be easily perceived as structures ofcharacter totally different from typical transient evoked com-ponents, not only because of their time span but also becauseof their frequency band narrower than for typical transientevoked components.

Examples of TF representations of SSOAEs for threesubjects are shown in Fig. 4, together with their spectra asprovided by the ILO system. The ILO spectrum is very noisyand sometimes it is difficult to determine definite spectralpeaks. The long-lasting components that correspond toSSOAEs are easy to identify on TF maps. The three dia-grams shown in Fig. 4 illustrate three different types of ob-served cases: one SSOAE, several SSOAEs, and noSSOAEs. The stable long-lasting components identified bythe MP procedure are marked as lines on Fig. 4. For the twoupper diagrams, there is good correspondence between theILO spectra and the TF maps. In the lower picture, in theILO spectrum, there are some lines that might be thought to
be SSOAEs. However, this is not the case as they are not


stable throughout whole 80 ms and the longest one dimin-ishes at about 55 ms. They actually correspond to the long-lasting decaying components.

Histograms showing frequency values for waveforms ofdifferent durations are shown in Fig. 5. The long-lastingcomponents are divided into two sets: decaying �time span5–40 ms� and stable ��40 ms�. The distributions of theshort-lived and stable components are rather similar, withsmall increases in the higher frequencies of long-lastingcomponents. The decaying long-lasting components are con-centrated in the range 1–2 kHz.

Table I shows the statistical data for the incidence ofSSOAEs and their long-lasting components. The percentageof subjects with SSOAEs was rather high, similar to the val-ues reported earlier in the literature �e.g., Sisto et al., 2001�.We have made a distinction between those components thatwere present during the whole recording and the decayingcomponents. Statistical data show that in each subject, atleast one long-lasting component was present. The contribu-

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FIG. 5. Distribution of frequencies of waveforms of different durations forthe 80 ms signal.

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sidered to be SOAEs, was high �74%�. However, the inci-dence of decaying components was almost 100%.

The amplitude of the OAE components and their timespan appear explicitly as parameters in MP decomposition,which permits the properties of selected waveforms to beestimated. In Fig. 6, the time evolution of the OAE compo-nents averaged over all the subjects is shown. The amplitudeof the short-lived components rapidly decays toward lowvalues for times �20 ms. Components of durations from5 to 40 ms decrease much more slowly. Components of du-ration above 40 ms after an initial rise �note that the first

TABLE I. Prevalence and average number �with standard error� of the long-lasting components of SSOAEs as detected by MP analysis.

Number of subjects 46Prevalence of stable long lasting components 74%Prevalence of decaying long lasting components 98%Prevalence of all long lasting components 100%Average number of stable long lasting components peremitting ear

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FIG. 6. Top: Averaged amplitude of all signals under investigation. �dotteddecaying components �5–40 ms�, �thin solid line� for long-lasting componcurves as fitted to the data for short-lived and longer-lasting decaying coexamined and exponentials gave the best results, yielding R2=0.98 for shor

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instant� and R =0.96 for decaying components �along=74e �.


4.5 ms are affected by the window of the ILO system� stay atan almost constant level, which suggests that they could be“real” SOAEs.

Several types of fits were applied separately to short-lived components and long-lasting components. It was foundthat the exponential functions gave best results. For short-lived components, the exponential fit: ashort=467e−0.18t

�where a is amplitude, and t—time instant� yielded R2

=0.98. For longer-lasting components, the dependence:along=74e−0.06t was found to result in an equally good fit�R2=0.96�. When both types of component were treated to-gether, the fit was much worse �R2=0.7�. However, when thedouble exponential aall=435e−0.18t+34e−0.003t was used, thefit improved significantly and a value of R2=0.98 was ob-tained. Note that the exponentials in this fit are similar tothose obtained for the fits for short-lived and decaying com-ponents alone. For long-lasting components, the parametersof the fitted curves are less similar. This is probably due tothe fact that when all components were treated together, theconstant amplitude part of the signal was also included. Dif-ferent functional dependencies for short- and long-lastingcomponents suggest that they have to be treated separately.

Much attention has been devoted to the frequency-latency curve and fitting of the power law functions to this

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all componentsshort componentsdecaying componentslong components

fit for short componentsfit for decaying components

for short-lived components �time span �5 ms�, �dashed line� longer-lasting��40 ms�, and �thick solid line� for all components. Bottom: Exponentialents �starting from the maxima of average amplitude�. Several fits were

d components �ashort=467e−0.18t, where a is the amplitude, and t is the time

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relationship �e.g., Tognola et al., 1997; Sisto and Moleti,2002�. In Fig. 7, latencies and time spans of TEOAEs forears with SOAEs �48 ears� and without SOAEs �44 ears� arecompared. The group with SOAEs was formed from ears inwhich long-lasting components of duration �40 ms were de-tected. The latency-frequency dependence for a group of earswithout SOAEs is power law. It forms a straight line on alog-log scale �Fig. 7�. The latencies of the group with SOAEsare slightly greater. This feature is particularly obvious in thecase of the 4 kHz band, where the difference in latenciesreached 1 ms and the test of significance yielded p�0.05.The relationship between frequency and time span revealedeven bigger differences between the groups with and withoutSOAEs.

The advantage of the MP method is that both short-livedand long-lasting components can easily be detected andseparated. The idea of removing long-lasting activity fromthe signal is presented in Fig. 8 for a subject having severallong-lasting components. In this figure, the TF distribution ofOAE energy for an ear with SSOAEs is illustrated. Below

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FIG. 7. Half-octave average latencies and time spans in relation to fre-quency for 20 ms TEOAE signals. The dataset was divided into two sets:ears with and without SOAEs.

this is shown the same signal after removing the long-lasting



components �of time span �5 ms�. The TF map thus ob-tained shows characteristics that suggest the logarithmic de-pendence of the latency-frequency curve.

The results presented above indicate that TEOAEs arestrongly influenced by SSOAEs in quite a high percentage ofcases and that the separation of both kinds of componentshelps in the interpretation of the experimental data.

V. DISCUSSION

Long-lasting components of evoked OAEs were studiedpreviously by Gobsch and Tietze �1993� and Sisto and Mo-leti �1999�. These authors applied spectral analysis using dif-ferent time windows to study changes in the amplitude anddecay times of the long-lasting components. In the studyreported herein, high-resolution TF analysis was carried outin order to investigate the evolution of different types oflong-lasting components over time. Long-lasting narrowbandcomponents in TF maps correspond well with the peaks ob-served in SSOAE spectra provided by an ILO system.

The advantage of the applied method does not rely onlyon the high-resolution TF representation. The signal compo-nents are parametrized in terms of frequency, amplitude, timeoccurrence �latency�, and time span, which permits the easyseparation of waveforms of different characteristics and thestatistical analysis of their properties.

The distribution of SSOAE frequencies obtained is verysimilar to the results presented recently by Kuroda �2007�.Sisto et al. �2001� found slightly different distributions: mostSSOAEs were located in the 0.8–3 kHz range and only afew components lie outside this area. One possible explana-tion for their findings is that their dataset consisted only ofmale subjects. The distribution of SSOAEs determined in ourstudy is also similar to those found for SOAEs measuredwithout synchronization by click stimuli �e.g., Burns et al.,1992�.

Distributions of frequencies of components of differentdurations are presented here for the first time. The MP algo-rithm provides a unique opportunity to construct histograms,not only for SOAEs but also separately for short-lived andlonger-lasting decaying components. This was made possiblebecause the time span of the components appears explicitlyas a parameter. Other methods, such as FFT and wavelettransform, do not permit this. The frequency histograms ofthe short-lived components agree with the spectral character-istics of this response �e.g., Sisto and Moleti, 2005�. Most ofthese fall within the region 1–2 kHz, which is similar to thefrequency range of SOAEs. For long time-span waveforms,there seem to be comparatively more high-frequency compo-nents.

The prevalence of SSOAEs in our study is similar to thatpresented in previous reports �e.g., Sisto et al., 2001� andsimilar to studies of SOAEs �e.g., Zhang and Penner, 1998�.It is well known that analysis of SSOAEs and SOAEs doesnot yield exactly the same results �e.g., Wable and Collet,1994�. Some peaks may only be identified in the measure-ment of SOAEs, while in the measurement of SSOAEs, ad-ditional components that are not visible in SOAE spectra
may be observed. SOAEs were not measured in our study,


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but on the basis of the properties of the components, we canspeculate that it is the long-lasting decaying components thatare responsible for the peaks that occur in SSOAE spectrathat are in addition to those that occur in SOAE spectra. Theincidence of decaying components is higher than the inci-dence of stable ones. Similar to SOAEs, they may play asubstantial role in the TEOAE signal.

Our analysis showed that evoked OAE consists of threetypes of activity: short-term components ��5 ms�, longer-lasting decaying components �5–40 ms�, and stable compo-nents ��40 ms�. This last category of activity is character-ized by energy that is practically constant in time and cantherefore be categorized as SOAEs.

Similar to our results, Sisto et al. �2001� observed twotypes of long-lasting component: ones that were present dur-ing the whole measurement period and decaying compo-nents. The waveforms of the longer decaying components�5–40 ms� identified here, correspond to the decaying com-ponents identified by Sisto et al. �2001�. The amplitude ofdecay was approximated by an exponential relationship�Sisto and Moleti, 1999�. In our study, exponential functionsyielded best fits to the short lived, as well as to the longer-lasting decaying components, albeit with different param-eters. The time evolution of 5–40 ms components bears aresemblance to TEOAEs �Fig. 6�. However, their narrowbandwidth makes them similar to SOAEs. Talmadge et al.

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FIG. 8. Example of a TF distribution of energy for a subject with multiple Spurely evoked part of the signal.

�1998� described slow-decay components as “resonant



TEOAEs.” We hypothesize that there are some kinds of reso-nant modes that bear a resemblance to SOAEs, but they haveto be evoked by stimuli and, not being self-sustained, theydecay with time. Short-lived components that appear inTEOAE also have the characteristic of resonant modes�Jedrzejczak et al., 2004�, which follows from the stability oftheir TF features. It appears that the cochlea has three differ-ent kinds of resonant mode: �1� very short duration andbroad bandwidth, �2� narrowband resonant modes decayingin time, and �3� sustained narrowband resonance modes ob-served as SOAEs.

There have been several attempts to automate the detec-tion of SOAEs based on FFT. Zhang and Penner �1998� pro-posed a procedure in which the spectrum was first flattenedover the whole frequency range and then peaks above athreshold were compared with the histogram of the noiseenergy. Later, Pasanen and McFadden �2000� presented analgorithm based on the identification of peaks that exceededa smoothed noise spectrum. Their results for the incidence ofSOAEs were similar to those of previous studies �e.g., Tal-madge et al. 1993�. In comparison with the spectral analysisused by these authors, the MP approach yields the additionalparameter of time span, which best distinguishes the differ-ent kinds of component. Further characteristics of SOAEs,

[ms]0 45 50 55 60 65 70 75 80

[ms]0 45 50 55 60 65 70 75 80

s. In the bottom map, long-lasting components were removed to reveal the

time4

time4

OAE

best visible on the high-resolution TF plots yielded by the



Redistrib

MP algorithm, are the narrow bandwidth and the stability offrequency. The analysis based on these features allows noiseto be separated efficiently from SOAEs.

SOAEs have a great influence on TEOAEs. It has beenshown that the amplitudes of TEOAEs are higher for sub-jects with SOAEs �e.g., Kulawiec and Orlando, 1995; Oster-hammel et al., 1996; Schmuziger et al., 2005�. SOAEs canalso influence the estimation of other parameters that areassociated with TEOAEs, such as latency. Their contributionto TEOAEs can produce additional maxima in the time evo-lution of TEOAEs. It can affect the estimate of latency ac-cording to its common definition, i.e., the time from the on-set of the stimulus to the maximum point on the envelope ofthe waveform �as described by Probst et al., 1991�. Thisartifact is not connected with any particular method, andwould have the same effect in terms of the latency shift, e.g.,for filtering, wavelet transform, or even when latency is cal-culated from tone-burst evoked OAEs. MP analysis that pro-vides an additional time-span parameter gives better controlover the latency estimate. It characterizes any discrepanciesof duration of OAE components. An example of this problemcould be the difference in the latency observed in full termcompared with preterm neonates as reported by Tognola etal. �2005�. It was later found that this feature was caused bythe higher number of long-lasting components in pretermneonates �Jedrzejczak et al., 2007�. After the removal of thelong-lasting components from the preterm data, thefrequency-latency dependence became similar for bothgroups. The effect of removing the long-lasting componentsfrom the TEOAE signal was more recently noted by Notaroet al. �2007�. Until recently, the influence of SOAEs onTEOAEs was removed only by excluding from the datasetears in which SOAEs have been detected �e.g., Hoth et al.,2007�.

VI. CONCLUSIONS

The prevalence of SSOAEs determined using MP analy-sis was similar to the results in previous reports that wereobtained using different methods. MP analysis can be used asa tool for the automated detection of SOAEs and is capableof distinguishing decaying components from stable ones.Many long-lasting components are stable over time, whichcan be used as an argument for their being real SOAEs. Byincreasing the acquisition window and applying TF analysis,one can combine the measurement of evoked and spontane-ous response. The spontaneous components can be removedfrom further analysis of the evoked response and their prop-erties can be described separately.

ACKNOWLEDGMENTS

The authors would like to thank David T. Kemp forproviding information about the structure of the data filescreated by the ILO equipment. This research was funded bythe Polish Ministry of Science and Higher Education withinthe framework of the 2007-2010 Scientific Support Program�Grant No. N N518 0924 33�.

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Synchronized spontaneous otoacoustic emissions analyzed in a time-frequency domain

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