Detection improvement for neonatal click evoked otoacoustic emissions by time–frequency filtering

Computers in Biology and Medicine 41 (2011) 675–686

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Detection improvement for neonatal click evoked otoacoustic emissions by time–frequency filtering V.W. Zhang a,b,n, Z.G. Zhang c, B. McPherson a, Y. Hu d, Y.S. Hung c a

Centre for Communication Disorders, The University of Hong Kong, Hong Kong Special Administrative Region, China National Acoustic Laboratories, 126 Greville Street, Chatswood, NSW 2067, Australia c Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong Special Administrative Region, China d Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong Special Administrative Region, China b

a r t i c l e i n f o

abstract

Article history: Received 4 November 2009 Accepted 4 June 2011

This study employed a time–frequency filtering technique to improve click evoked otoacoustic emission (CEOAE) detection at lower frequency bands, and hence to reduce the number of referral cases in neonatal OAE screening. Using this approach the detectability of CEOAEs, in terms of lower frequency SNRs and whole wave reproducibility, was significantly improved. Evaluations of screening outcomes demonstrated this method significantly reduced the overall referral rate, by 2.5 percentage points in initial CEOAE hearing screening. This approach may have potential application in OAE technology and in neonatal hearing screening programmes. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Click evoked otoacoustic emissions Denoising Time–frequency analysis Time–frequency filtering Universal neonatal hearing screening Wavelet transform

1. Introduction The otoacoustic emission (OAE) response is the acoustic energy produced by the mechanical action of the outer hair cells, and is measured from the outer ear canal by a sensitive microphone [1]. As the OAE test is quick, objective and noninvasive, this measurement has been widely used in universal neonatal hearing screening (UNHS). Click evoked otoacoustic emission (CEOAE) measurement, as one type of OAE, is evoked using a broad bandwidth click stimulus which consequently stimulates a wide frequency region of the cochlea in a single measurement. CEOAE testing has been especially applied as a general tool for newborn hearing screening. Despite great achievements having been made in recent years, some practical aspects of OAE assessment still remain to be explored. One important area for further OAE research is to develop more rapid, reliable screening techniques and refine screening algorithms and equipment [2,3]. As the evoked OAE signal has a small amplitude, typically from the –20 to 20 dB SPL range, noise often obscures recorded CEOAEs, particularly for lower (from 500 to 1500 Hz) frequencies [4–8]. Previous strategies for noise reduction were mainly conducted in either the time or frequency domain only, such as

n Corresponding author at: National Acoustic Laboratories, 126 Greville Street, Chatswood, NSW 2067, Australia. Tel.: þ61 2 94126735; fax: þ61 2 94118273. E-mail address: [email protected] (V.W. Zhang).

0010-4825/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.compbiomed.2011.06.003

signal averaging [1,9–11], artifact rejection by derived nonlinear mode [12], time window adjustment [13–15], band filtering [16,17], and an adaptive noise canceling (ANC) technique [18]. Although time-domain signal averaging has become the basic method to improve signal-to-noise ratio (SNR) in current CEOAE recording systems, this technique needs increased test time in noisy environments to collect more responses for averaging. This requirement is not consistent with the main imperative of decreasing screening time (and hence data acquisition time) in neonatal hearing screening programmes. Also, Berninger [9] found that increasing the number of averages does not result in a significant improvement in SNRs at low frequency regions. In addition, although a shorter time window and/or high-pass filtering have been commonly applied to reduce low frequency noise in currently available CEOAE equipment, these methods still have limitations in achieving clearer CEOAE detection in low frequency ranges. This is partly due to the fact that these processes are conducted in either the time or frequency domain, which makes it hard to achieve an optimal analysis compromise when considering the entire noise effects in the joint time– frequency domain. It was reported that the mean prevalence rate of observed CEOAEs at a 1 kHz center frequency was below 40%, even in a group of neonates who, overall, passed UNHS [8]. This drawback may constrain the utility of CEOAE measures in the accurate assessment of certain hearing disorders, such as those involving low to mid frequency hearing loss [19–21]. As to its application in UNHS, the low prevalence rate of observable CEOAE

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responses in the low frequency range may also adversely influence the overall pass rate and/or false positive rate of hearing screening. Therefore, the above concerns lead to the need to consider alternative signal processing methods to overcome the effects of noise on recording CEOAE responses. As CEOAEs are time-varying signals with frequency dispersion along time, several time–frequency analysis (TFA) techniques, such as short-time Fourier transform (STFT) [22–24], Wigner– Ville distribution (WVD) [25,26], matching pursuit (MP) [27,28] and wavelet transform (WT) [29–34], have been proposed to represent the characteristics of a CEOAE signal in the joint t–f domain. Among these TFA methods, WT technique was demonstrated to have better t–f resolution for CEOAE signal analysis than the STFT and other algorithms [31,35–37]. In addition to its power in signal analysis, WT method is also useful for the detection and estimation of noisy signals. The denoising procedure for a discrete wavelet transform (DWT) is mainly described as having three steps [38,39]: first, to decompose the signal with a selected suitable mother wavelet and selected levels of decomposition; then to determine the corresponding coefficients at each level which are considered as noise contributions, and eliminate such coefficients which are below a certain threshold of magnitude; finally, to compute an inverse wavelet transform using the modified coefficients to obtain a new version of the original data. With CEOAE recordings, the WT algorithm can be used to decompose the original signals into a series of scales. At different scales, adjustable time windows were employed to account for the most probable locations for CEOAE components [13,15,34,40]. Motivated by the concept of a DWT denoising process for CEOAEs, a two dimensional (2D) t–f filtering method based on the t–f characteristics of normal neonatal CEOAE responses was proposed in this study. Unlike DWT-based denoising that decomposes the signal into different scales, a 2D t–f filtering technique has been introduced to perform a t–f mask to separate and then filter the signal and noise directly in the t–f domain. That is, once the ‘‘t–f signal subspace’’ is determined, the components that lie outside this region can be viewed as ‘‘t–f noise subspace’’ and be suppressed. Or, if the t–f patterns of noise are known, the noise components in specific t–f areas can be identified directly and set to zero. Therefore, regional noise will be substantially reduced in the t–f domain, and the denoised signal can be derived by means of an inverse t–f transform [41,42]. The 2D t–f filtering approach has been applied to many signal processing areas [43] and was found to be an efficient tool for SNR enhancement [42]. However, no known work has been undertaken that makes use of this technique for CEOAE signals or in relation to CEOAE hearing screening. The aims of this study were to develop a t–f filtering method for denoising neonatal CEOAE recordings in lower frequency ranges (o1.5 kHz), and then to evaluate the denoising performance of this procedure with a large CEOAE screening data set.

2. Materials and methods 2.1. Subjects A total of 557 neonates (1113 ears, including 557 left ears and 556 right ears) from well-baby nurseries were enrolled at the Henan Provincial People’s Hospital, China. The mean age at test was 3.3 days (SD ¼1.3). 53.9% of ears were male and 46.1% were female. All the neonates were recruited into the research program after parents elected to participate on a voluntary basis. Written informed parental/legal guardian consent was obtained prior to subject enrollment in the research project. None of the newborns had any risk factors for hearing disorder.

A valid, passing CEOAE-only response measurement was one that fulfilled the following CEOAE criteria: stimulus stabilityZ75%; whole wave reproducibilityZ70%; at least three of five test frequency bands centered at 1, 1.4, 2, 2.8 and 4 kHz with SNRZ3 dB [44]. Similar criteria have been used by other researchers [45–47]. According to the conventional, clinical CEOAE screening outcomes, 1031 ears from 1113 ears passed the initial CEOAE measurement and 82 ears required referral. 2.2. Apparatus and parameters All the measures were recorded in a non-sound treated room adjacent to the nursery ward. The average ambient room noise level with OAE equipment in operation was under 45 dBA. Neonates were tested while in natural sleep or a quiet state. CEOAE data were collected using the Echoport ILO 292 USB system with V6 software (Otodynamics Ltd., UK) installed in a laptop computer. All the CEOAE stimulus and response levels were recorded by a standard ILO system clinical neonatal probe, which was calibrated before every test session. CEOAE measurement used the ILO system default nonlinear ‘‘QuickScreen’’ acquisition mode, which has been commonly employed in UNHS programmes. To elicit the CEOAE response, alternating click stimuli driven by a biphasic triangular pulse of 300 ms duration were presented at a repetition rate of approximately 78 clicks per second. A recorded stimulus level of 75–80 dB equivalent sound pressure level (peSPL) in the ear canal was considered acceptable for CEOAE measures. The recordings were filtered with the ILO 292 USB system default procedure (a single order HP reverse filter at 300 Hz, then a second-order band filter from 400 Hz to 6400 Hz). CEOAE recordings from the ILO USB system were digitized at a rate of 20,000 samples/s, resulting in 256 sampled points, windowed 12.8 ms. Around the first 2–3 ms of the 12.8 ms analysis window was eliminated to reduce the contribution of possible stimulus artifact to the average response waveform. The CEOAE response stopping criteria required a minimum of 70 OAE sweeps to be averaged. If no clear response (low amplitude of the signal and/or high noise level) was noted at 70 presentations, up to 260 responses were obtained. The noise rejection level was generally set lower than 8 mPa (52 dB SPL). The recorded overall noise level and response level were 10.273.3 dB SPL and 18.6 79.6 dB SPL, respectively. 2.3. Time–frequency analysis and denoising algorithm The details of the temporal analysis and frequency analysis by the ILO system are presented in the Appendix. The principal steps for low frequency noise reduction were to (1) map the original CEOAE recordings on the t–f domain by CWT, (2) localize the specific area of lower frequency noises on the t–f domain and decay them using a 2D t–f filter, then (3) reconstruct the processed signals using an inverse continuous wavelet transform (ICWT). A flowchart for this process is shown in Fig. 1 and the details of this process are described below. Step 1: map the original CEOAE recordings As the presence of CEOAEs in the ILO system is determined by identification of waveforms in two buffers, the averaged recording A and B (containing noise components and CEOAE signal) is mapped separately into the t–f domain by CWT in order to be further compared with the original CEOAE calculated by the ILO system. Comprehensive descriptions of WT for OAE recordings have been given by several researchers [29,31,32]. This study uses CWT to describe pffiffiffiffiffiffiffiffiffi nCEOAE recording x(n), x ¼A or B, by R Pðn,f Þ ¼ t xðtÞ f =f0 c ðf =f0 ðtnÞÞdt, where n and f are the time and frequency index, respectively. c(  ) is the mother wavelet

V.W. Zhang et al. / Computers in Biology and Medicine 41 (2011) 675–686

Step 2

Step 1 CWT

Averaged CEOAEs in A buffer

Correlation calculation & Power spectrum display

Step 3 2D T-F Denoising

T-F display for A waveform

2D T-F Denoising

T-F display for B waveform

CWT

Averaged CEOAEs in B buffer

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Save pre denoisedparameters

Denoised T-F display

ICWT

Denoised T-F display

ICWT

Denoised A waveform

Denoised B waveform

Correlation calculation & Power spectrum display

Save post denoisedparameters

Parameters comparison

Fig. 1. Flowchart of the proposed 2D time–frequency denoising process.

6

5

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Scalogram of Noise (case 3)

Scalogram of Noise (case 2)

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Fig. 2. Time–frequency representation for noise in three CEOAE examples.

function with central frequency f0. The mother wavelet function c(  ) used in this study was proposed by Tognola et al. [31,48], and is a modulated cosine function cðnÞ ¼ ð1=ð1 þ t b ÞÞcosðanÞ. The TFA of CEOAEs have been shown to achieve the best results when b ¼4 and a ¼20 are used [31,48]. The squared magnitude of P(n, f) is called the scalogram. At different frequencies, WT uses the wavelet function with different window sizes, which are obtained by scaling the wavelet function in the frequency domain, to yield an adaptive t–f resolution. The adaptive window size is obtained by shifting the mother wavelet function in the time domain and scaling the mother wavelet function in the frequency domain. Step 2: reduce the lower frequency noise It has been demonstrated that for different components of a CEOAE response an intimate relationship between latency and frequency exists: the higher the frequency the shorter the latency [49,50–53]. The latency is defined as the time when the absolute value of the wavelet coefficient reaches its maximum [54]. The definition for ear-averaged latency is ear-average of the

individual latencies tk ðfi Þ (k of total nk ears) at a particular frequency band fi [52]: Ear-averaged latency tðfi Þ ¼

nk 1 X t ðf Þ nk j ¼ 1 k i

ð1Þ

For adults, the frequency components of CEOAEs at 1–1.5 kHz show longer latencies, compared to higher frequency ranges [28,31,48,55]. A similar pattern was also found in neonatal ears [50,51,53]. With regard to CEOAE latency between normal and hearing impaired ears, a significant difference has been observed in both adult [56,57] and neonatal ears [50,52,53]. The estimated noise component xN (according to the temporal definition of the ILO system) can also be transformed into the t–f domain by CWT. Fig. 2 demonstrates that noise is always located at lower frequency ranges, and features with frequency dispersion along the time axis are not evident.

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Fig. 3. (a) Distribution of CEOAE components in t–f domain and the location of the 2D t–f mask function. The relationship between latency and frequency was plotted as a closed triangle, and it was fitted by an exponential mode of curve estimation. The distributions of individual peak location were shown as a closed circle. The shadow area was the proposed 2D window for further denoising process. (b) 2D and 3D illustrations of proposed mask function.

According to the above t–f features of CEOAE signals and the noise distribution on the t–f domain, a 2D filtering in the t–f domain was then considered to achieve noise reduction in the lower frequency ranges. Fig. 3a refers to previous work based on data from 200 normal neonates with CEOAE responses at all five centered frequency bands having SNR Z3 dB [33]. The relationship between latency and frequency for normal full term neonatal CEOAEs was fitted by an exponential mode of curve estimation: t ¼ b0 eb1 f , where f in kHz and b0 ¼9.71, b1 ¼–0.14. The statistical parameters for this curve estimation are R2 ¼ 0.91, df ¼7, F¼70.82 and Sig¼ 0.00. According to this equation, the latencies corresponding to center frequencies at 1, 1.4, 2, 2.8 and 4 kHz were calculated at around 8.42, 7.96, 7.31, 6.52 and 5.5 ms, respectively. Based on the t–f distribution P(n, f) of x(n), we aim to preserve the desired signal components and remove unwanted noise contribution by applying a mask function P0 (n, f)¼ P(n, f)W(n, f), where W(n, f) is a 2D mask function having an approximate rectangular shape in the t–f domain, as shown in Fig. 3b. In the present study, the mask function W(n, f) is defined as

8 1 > > < 0 Wðn,f Þ ¼      > DnÞ pðf fcutoff Df Þ > : 14 1cos pðnncutoff 1cos Dn Df

where ncutoff and fcutoff are, respectively, cutoff time and frequency, while Dn and Df are used to generate a smoothed buffer area in the mask function to avoid a hard window edge and spurious high-frequency components during the signal reconstruction. Here, the parameters of Dn and Df, are set as 1 and 200, respectively. As seen from Eq. (2), the principal step of the proposed 2D t–f filtering method is to find a specific t–f area where the noise components dominate the energy power compared to the signal components, and then to use a 2D mask to cutoff these noise components in the t–f area. Considering that the significant frequency components of noise are generally below 1.5 kHz [4–8], the frequency cutoff threshold fcutoff of the 2D t–f filtering was set as 1.5 kHz. On the other hand, the cutoff time of the t–f area can be determined as the time instant before which the difference in proportion of noise energy and signal energy is maximum. Since the aim was to find a t–f area which contains as much noise energy as possible and as little signal energy as possible, there was a need to further

,

n 4ncutoff þ Dn,f 4 fcutoff þ Df

,

n oncutoff Dn,f ofcutoff Df

,

others

ð2Þ

V.W. Zhang et al. / Computers in Biology and Medicine 41 (2011) 675–686

679

Fig. 4. Normalized, ear-averaged energy changes with the time for signal and noise at frequency band below 1.5 kHz frequency range.

study the energy distributions of the signal and noise in the lower frequency area. If the signal distribution is relatively concentrated compared to low frequency noise, we could choose a cutoff value as close as the start time point of a signal in order to keep more expected signal and cut more expected noise. To choose an optimal cut-off value in the time domain, the normalized ear-averaged energy distributions along with time (before the energy peak locations) for signal and noise components in the frequency band below 1.5 kHz were estimated. This normalized energy distribution along the time axis was calculated as the time instants where the ear-averaged energy in the t–f domain reaches a series of proportions of the peak energy value. Based on 200 normal CEOAE recordings with SNR passing at all five test frequency bands centered at 1, 1.4, 2, 2.8 and 4 kHz [33], Fig. 4 illustrates the normalized ear-averaged energy distributions of signal and noise, and it provides a rough description of their energy distributions in the low frequency range (o1.5 kHz) along the time axis. As seen from Fig. 4, for example, the ear-averaged energy peak (100% of the peak energy value) of signal components is located around 8.42 ms, and the 10% of the peak energy of the signal is around 4.02 ms. It was also found that the start time of 1% of peak energy for noise was around 0.5 ms. This may indicate that low frequency noise should exist over the whole time-axis area. The relationship between the normalized energy and time can be fitted by cubic curve estimation and the functions for signal and noise are also illustrated in Fig. 4. As the basic principle for denoising is to cut-off a greater proportion of noise and retain useful information as much as possible, the maximum difference between the energy proportion of noise and signal which was calculated to be around 5.0 ms was selected as cutoff time (ncutoff) in the 2D mask function. In addition, the literature on t–f characteristics of neonatal CEOAE recordings has also noted that the latencies of lower frequency components (below 1.5 kHz) of CEOAE were generally longer than 7 ms [49,50–53]. In other words, the 1–1.5 kHz frequency components of normal neonatal CEOAE signals may only rarely have shorter latencies than 5 ms. Therefore, 5 ms might be a reliable value to cutoff a greater proportion of ‘‘true’’ noise in the lower frequency range, and also avoid substantial signal energy loss. Step 3: reconstruct the CEOAE signals The above processed CEOAE signals in A and B buffer are finally reconstructed into the time domain by ICWT from the denoised t–f distribution P0 (n, f) x0 ðnÞ ¼

Z Z t f

P 0 ðn,f Þ

qffiffiffiffiffiffiffiffiffi f =f0 cðf =f0 ðtnÞÞdfdt

ð3Þ

Then, the corresponding parameters in either time or frequency domain can be further recalculated and compared to the original recordings created by the ILO system.

2.4. Evaluation and statistical analysis In this study, all raw CEOAE data obtained by ILO V6 software were offline transferred into MATLAB programs (Mathworks MATLAB software version 7.0), which were developed in-house for further signal processing. The performances of the proposed 2D denoising method were mainly evaluated by means of the following quantitative parameters in classifying the pass and refer groups: the whole wave reproducibility in the time domain, and the SNRs of CEAOEs in the frequency domain. These parameters have been widely used clinically as the key indices for pass/fail criteria in UNHS programmes. To provide an easy and direct comparison for clinicians, the present study finally transferred the processed data from the t–f domain into waveform and conventional power spectrum displays to illustrate the results. Calculation of signal, noise and SNR values was performed identically for pre- and post-denoising waveforms. Evaluations in the frequency domain were carried out using a half-octave band power spectrum display with frequencies centered at 1, 1.4, 2, 2.8 and 4 kHz. At different frequency bands, the differences between pre- and post-denoising for the recorded noise level, signal amplitude and SNRs were compared using a paired-samples t-test. As whole waveform reproducibility data were not normal distributed, the statistical evaluation used nonparametric Wilcoxon tests. The w2-test was applied to compare the changes in the overall pass rate for CEOAE screening. Statistical analyses were performed using SPSS for Windows version 12.0 software and statistical significance was set at p o0.05.

3. Results 3.1. Analyses for whole wave reproducibility Whole wave reproducibility was computed by correlation between waveforms derived from the two memory buffers. The Wilcoxon test analyses indicated that whole wave reproducibility was significantly improved after t–f denoising in each group (overall data: z ¼–27.7, po0.05; pass group: z¼–27.8, p o0.05; refer group: z¼ –5.3, p o0.05) (Table 1). The mean improvement in reproducibility was 3.8 percentage points in each group.

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3.2. Comparison of noise, signal and SNRs pre- and post-denoising at lower frequency bands The pre- and post-denoising results for noise level at lower frequency bands are illustrated in Table 2. The statistical analysis with paired t-tests indicated that noise level at 1 and 1.4 kHz frequency bands were significantly reduced after denoising in each group [overall data: at 1 kHz: t (1112)¼68.6, po0.05; at 1.4 kHz: t (1112)¼49.3, po0.05. Pass group: at 1 kHz: t (1030)¼ 65.8, po0.05; at 1.4 kHz: t (1030)¼47.0, po0.05. Refer group: at 1 kHz: t (81)¼19.4, po0.05; at 1.4 kHz: t (81)¼15.5, po0.05]. The pre- and post-denoising results for signal amplitude at lower frequency bands are shown in Table 3. The statistical analysis with paired t-tests indicated that the signal at 1 and 1.4 kHz frequency bands was also significantly reduced after denoising in each group [overall data: at 1 kHz: t (1112)¼ 60.6, po0.05; at 1.4 kHz: Table 1 Whole reproducibility (%) (mean7 SD) for original CEOAE and denoised CEOAE in different groups. Total ears (N ¼1113) Pass (n¼1031) Refer (n¼ 82) CEOAE Denoised CEOAE D (percentage points) p

84.4 7 18.1 87.8 7 17.5 3.4 o0.05

88.47 7.8 91.77 6.0 3.3 o 0.05

33.5 729.6 38.3 732.3 4.8 o 0.05

Note: D denotes the mean improvement in reproducibility.

Table 2 Mean (7 SD) noise level (dB) for original and denoised CEOAE recordings at 1 and 1.4 kHz frequency bands in different groups.

Pass (n¼ 1031)

Pre-denoising Post-denoising

D p Refer (n¼82)

Pre-denoising Post-denoising

D p Total (N ¼1113)

Pre-denoising Post-denoising

D p

1 kHz

1.4 kHz

3.69 7 4.71 1.69 7 4.87 2.0 o0.05

0.94 74.95  0.177 5.00 1.11 o 0.05

2.53 7 4.46 0.51 7 4.61 2.02 o0.05

 0.317 5.04  1.51 75.12 1.2 o 0.05

3.60 7 4.70 1.60 7 4.86 2.0 o0.05

0.84 74.97  0.277 5.02 1.11 o 0.05

Note: D denotes the differences between pre- and denoising on mean noise level.

Table 3 Mean (7SD) signal amplitude (dB) for original and denoised CEOAE recordings at 1 and 1.4 kHz frequency bands in different groups.

Pass (n¼ 1031)

Pre-denoising Post-denoising

D p Refer (n¼82)

Pre-denoising Post-denoising

D p Total (N ¼1113)

Pre-denoising Post-denoising

D p

1 kHz

1.4 kHz

4.76 7 4.56 3.07 74.71 1.69 o 0.05

10.257 5.52 9.777 5.65 0.48 o 0.05

0.33 74.19 –1.46 7 4.38 1.79 o 0.05

–1.36 74.30 –2.31 74.34 0.95 o 0.05

4.43 7 4.68 2.73 7 4.84 1.7 o 0.05

9.397 6.22 8.887 6.39 0.51 o 0.05

Note: D denotes the differences between pre- and denoising on mean signal amplitude.

t (1112)¼36.7, po0.05. Pass group: at 1 kHz: t (1030)¼58.2, po0.05; at 1.4 kHz: t (1030)¼36.2, po0.05. Refer group: at 1 kHz: t (81)¼17.2, po0.05; at 1.4 kHz: t (81)¼12.3, po0.05]. To investigate whether the denoised CEOAE signals were clearly associated with the originals at lower frequency bands, a scatterplot was drawn (Fig. 5). At both 1 and 1.4 kHz, the points fit the linear regression line well. The statistical analysis showed that the signals pre- and post-denoising were significantly correlated [overall data: at 1 kHz: r ¼0.981, p o0.05; at 1.4 kHz: r¼ 0.998, p o0.05. Pass group: at 1 kHz: r ¼0.961, po0.05; at 1.4 kHz: r ¼0.994, p o0.05. Refer group: at 1 kHz: r ¼0.95, po0.05; at 1.4 kHz: r ¼0.974, p o0.05]. Using the 2D t–f filtering method, the overall mean SNRs at 1 and 1.4 kHz center frequency bands were improved by 0.3 and 0.6 dB, respectively. Despite the values of SNR changes being low compared to the changes in signal amplitude and noise level, the proposed denoising protocol achieved the goals of significantly improving SNRs [at 1 kHz: t (1112)¼  8.06, po0.05; at 1.4 kHz: t (1112)¼  23.13, po0.05]. The improvements of SNRs were also significant in both pass and refer groups in the above two frequency bands [in pass group, at 1 kHz: t (1030)¼–8.01, po0.05; at 1.4 kHz: t (1030)¼  23.3, po0.05; in refer group, at 1 kHz: t (81)¼ 1.24, po0.05; at 1.4 kHz: t (81)¼  2.77, po0.05] (Table 4). Fig. 6. illustrates in detail the denoising performance of one original referred CEOAE recording. The results clearly show that the whole wave reproducibility was improved from an initial 65.6% to 76.1% (over the pass threshold) when denoising was performed. Also, all five frequency bands passed the SNR criterion with SNRZ3 dB after denoising and the case would be reclassified into the pass group. 3.3. Analyses for screening outcomes The relationships of individual CEOAE screening outcomes pre- and post-denoising are shown in Table 5. For the overall data, there was 95.2% (28.3%þ66.9%) and 96.8% (75.6% þ21.2%) agreement between the pre- and post-denoising results at 1 and 1.4 kHz, respectively. In addition, 41 ears (3.69%) at 1 kHz and 32 ears (2.89%) at 1.4 kHz had a CEOAE fail result but the postdenoising program gave a pass result. Table 5 also shows that, 12 ears at 1 kHz and 4 ears at 1.4 kHz had a pre-denoising CEOAE pass but the denoising processing gave failed results. As to the overall pass rate for CEOAE screening before denoising, 1031 ears (92.6%) from 1113 neonatal ears fulfilled the pass criteria and 82 ears (7.4%) required referral. Among these ears, 64 ears failed both whole wave reproducibility and SNR criteria, and 18 ears failed either reproducibility or SNR parameters. Using the t–f denoising, 27 additional ears from 82 CEOAE failed data would pass screening after denoising. Therefore, a total of 55 ears would fail the denoised CEOAE screening finally (Table 6). The referral rate for screening after using t–f denoising is 4.9% and was therefore significantly reduced—by 2.5 percentage points— compared to the rate for conventional CEOAE screening [w2(1, N ¼2226)¼5.67, p o0.05]. Clinical CEOAE rescreening (before discharge or within one month post-discharge) confirmed that 18 out of 27 ears that failed conventional screening and passed after denoising also passed at the second conventional screening occasion. The remaining 9 ears in this category could not be followed up. Detailed information on follow-up testing of the subject group and the improvement in screening outcome for the relevant ears is summarized in Fig. 7. 4. Discussion This study proposed a 2D t–f filter to improve the precision of CEOAE detection in lower frequency ranges, and concurrently

V.W. Zhang et al. / Computers in Biology and Medicine 41 (2011) 675–686

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Fig. 5. Signal components at 1 and 1.4 kHz frequency bands pre- and post-denoising.

reduce the referral rate in initial CEOAE neonatal hearing screening. To our knowledge no study has reported the effect of such denoising techniques on large-scale neonatal CEOAE screening data sets.

In general, filtering is a commonly used method in the frequency domain to either reject or pass signals whose frequencies fall in a specified band or whose frequencies are above or below specified limits [58]. If the signal and/or noise components

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have specific patterns in the t–f distribution, a denoising technique based on TFA methods would be very useful. As random noise tends to spread evenly into the entire t–f domain, while the signal energy usually concentrates in a relatively small region [42], the signal and noise may be separated and to be further filtered directly in the t–f domain. The key concepts of this method can be described as: (1) decompose the noisy signal into the t–f domain via one of the TFA methods; (2) define the t–f signal subspaces and unwanted noise region and (3) apply a mask function to filter out noise. Using t–f filtering, the regional SNR may be substantially improved in the t–f domain, and the denoised signal can be transformed from the t–f domain back to either time or frequency domains. As for the CEOAE signal, it is often intimately related to the click stimuli and the different frequency components appear with relatively fixed latency. The competing noise is generally unrelated to the stimulus. Thus, an application that utilizes this

difference to separate the signal and noise in t–f domain, in order to improve the SNRs in particular lower frequency bands, may be of value. The denoising procedure involved an important step that was to select a ‘‘suitable’’ cutoff threshold to balance the trade-off in the results. If the chosen values for these threshold parameters are too small, then the noise will still be the dominating factor in the lower frequency regions. If the selected parameter values are too large, then the signal contents will be overly reduced. In this study the designed t–f filtering system had cutoff thresholds for

Table 5 Relationship (numbers of ears and percentages) of CEOAE screening outcomes between pre- and post-denoising at 1 and 1.4 kHz frequency bands for overall data (N ¼ 1113 ears). Freq. bands (kHz) 1

Table 4 Mean (7 SD) SNRs (dB) for original and denoised CEOAEs at 1 and 1.4 kHz frequency bands in different groups. 1.4

Pass (n¼ 1031)

CEOAE Denoised CEOAE

D p Refer (n¼82)

CEOAE Denoised CEOAE

D p Total (N ¼1113)

CEOAE Denoised CEOAE

D p

1 kHz

1.4 kHz

1.077 5.00 1.38 75.35 0.31 o 0.05

9.31 7 6.87 9.95 7 7.04 0.64 o0.05

 2.27 2.76  1.977 2.92 0.23 o 0.05

 1.04 73.40  0.80 73.69 0.24 o0.05

0.837 4.95 1.13 75.28 0.3 o 0.05

8.55 7 7.22 9.15 7 7.40 0.6 o0.05

CEOAE pass

CEOAE refer

Denoised CEOAE pass Denoised CEOAE refer

315 (28.3%)

41 (3.7%)

Denoised CEOAE pass Denoised CEOAE refer

841 (75.6%)

Kappa value (j) 0.89

12 (1.1%) 745 (66.9%) 32 (2.9%)

0.91

4 (0.4%) 236 (21.2%)

Table 6 Relationship (numbers of ears and percentages) of CEOAE screening outcomes between pre- and post-denoising (N¼ 1113 ears).

Denoised CEOAE pass (n ¼1058) Denoised CEOAE refer (n¼ 55)

CEOAE pass (n¼ 1031)

CEOAE refer (n¼ 82)

1031 (92.6%)

27 (2.5%)

0 (0%)

55 (4.9%)

Note: D denotes the mean improvement in SNR.

Fig. 6. An example of denoising performance from a referred neonatal CEOAE recording. The left two figures are the original CEOAE recording. The right two figures are the denoised CEOAEs. (A) The comparison of whole reproducibility for CEOAE response pre- and post-denoising. (B) Spectrum of 1/2 octave frequency band display of pre- and post-time–frequency denoising. Blue color depicts the CEOAE signal and red color depicts the noise floor. The numbers refer to the SNR (dB) at each center frequency band. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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First time screening CEOAE failed ears (n = 82 ears – A: 5; B: 13; C: 64)

Failed t-f denoising methods (n =55 ears)

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Notes: A: Repro>70% and SNR Fail B: Repro<70% and SNR Pass C: Repro<70% and SNR Fail

Passed t-f denoising method (n = 27 ears – from A: 5; B: 13; C: 9)

Passed CEOAE rescreening (n = 23 ears)

Passed CEOAE rescreening (n = 18 ears– from A: 5; B: 7; C: 6)

Failed CEOAE rescreening (n = 9 ears) Failed CEOAE rescreening (n = 0 ears) Need diagnosis (n = 9 ears)

Could not follow-up (n = 23 ears)

Could not follow-up (n = 9 ears – from A: 0; B: 6; C: 3)

Follow-up tests Fig. 7. Flowchart of the initial screening outcomes and the follow-up results for the CEOAE referred group (n¼ 82 ears) tested.

time and frequency of 5 ms and 1.5 kHz, respectively. The frequency components of noise superimposed on the CEOAE recording located at 0–1.5 kHz with 0–5 ms latencies was assumed to be the ‘‘true’’ noise and then reduced by the 2D t–f filtering acting across both time and frequency. Although the t–f characteristics of real CEOAE data may differ from case to case, a mask (window) function determined based on the averaged data should also be effective, as has been shown by the experimental results. A total of 1113 neonatal CEOAE data were used to evaluate denoising performance. The results demonstrated that the proposed t–f denoising approach significantly improved the detectability of CEOAE recordings. In the refer group, overall whole wave reproducibility was increased by 4.8 percentage points, but the mean value was still very low and did not meet the pass criterion of 70% reproducibility. This result implies that the 2D t–f filtering technique improves the whole reproducibility of the recordings without altering the basic features of the responses. Similar to the results for whole wave reproducibility, the changes for SNR in the refer group did show an improvement difference at 1 and 1.4 kHz frequency bands, but the mean value of SNR was still low. The denoising process did not bring abnormal benefits to mean SNR value at these two frequency bands for the referral cases. The individual screening outcomes showed that no screening results changed from pass group to refer group after denoising, which implies that the initial referral rate for CEOAE screening was not increased using the t–f filtering approach. At lower frequency bands, however, a total of 16 ears from 1113 data (1.4%) were found that initially passed the SNR criterion but decreased to be lower than 3 dB after denoising. This may because the denoising procedure extracted the noise together with additional CEOAE signal in the same t–f region. Another reason may originate from the signal definition in the ILO system, as it is

possible that the ILO system treats some ‘‘true’’ noise as CEOAE signal when the noise components at a specific frequency area are similar for both A and B buffer waveforms. All these 16 ears were from the clinical pass group and would not have transferred to the refer group after denoising, as they still met the pass criteria of SNR Z3 dB in at least three of five frequency bands as well as having whole wave reproducibilityZ70%. Although SNRs for these 16 passed ears were reduced, 6.6% (3.7þ2.9%) of all ears would have passed at 1 and/or 1.4 kHz with better SNR results after denoising. Hence, CEOAE detectability was improved at the above two frequency bands using the t–f filtering method. As to the overall pass rate for CEOAE screening, the proposed method was shown to be a promising approach to increase the pass rate and reduce false positive cases in neonatal CEOAE screening, although the sensitivity and specificity of this denoising approach could not be evaluated due to constraints in our ability to monitor later diagnostic test results. From Fig. 7, it also can be found that the ears that failed CEOAE screening by either whole wave reproducibility or SNR clinical criteria often had a clear denoised response and were reassigned to the pass category after denoising. For the 64 ears that failed both reproducibility and SNR parameters, the t–f filtering approach only passed 9 ears. This indicates that the proposed t–f denoising method would generally not pass cases that had very poor CEOAE recordings. The proposed method therefore has important clinical implications. For example, the National Bureau of Statistics of China in 2007 estimates 15.89 million neonates are born in the 31 cities/ provinces of China each year.1 If the referral rate (by ear) is calculated as 7.4%, about 2.35 million ears may fail first time CEOAE screening. Based on the above results, which indicate that

1

http://www.stats.gov.cn/tjsj/ndsj/2007/indexce.htm.

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32.9% of the conventional referral cases would pass after denoising, the proposed approach would result in nearly 773,150 additional ears passing initial screening. This would greatly reduce the heavy follow-up burden on the health care system of rescreening and/or diagnostic assessment and also decrease anxiety in many families.

5. Summary A 2D t–f denoising technique that incorporates a CWT method was presented and then evaluated in this study. using this approach, the detectability of neonatal CEOAEs in terms of SNRs in lower frequency ranges and whole wave reproducibility was improved, and low-frequency noise was reduced. Evaluations of screening outcomes demonstrated this method significantly reduced the overall referral rate by 2.5 percentage points in first time CEOAE hearing screening. Clinical application of the technique may reduce the heavy follow-up burden associated with neonatal hearing health care. In addition, the denoising processing was fast and easy to implement, and the whole procedure could be incorporated in CEOAE recording devices, or could be conducted offline. Thus, the proposed 2D denoising protocol has potential application in OAE technology as a means of enhancing the quality of recorded CEOAE responses, and reducing referral cases and hence false positive rates, in neonatal hearing screening programmes.

Conflict of interest statement None declared.

Acknowledgments The authors wish to thank Mr. Oliver Brill and the scientists of Otodynamics Ltd. (UK) for their generous assistance in providing detailed OAE parameters and algorithms for the ILO system. We are grateful to Chris X.G. Wang, Dr. S.C. Chan and the anonymous reviewers for their invaluable comments on an earlier version of the manuscript. The work was partially supported by a Grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (HKU7434/04M).

Appendix Temporal analysis by ILO system The CEOAE response can be displayed as the time-averaged waveform after the onset of the transient stimulus. In the default ILO nonlinear mode, the CEOAEs are acquired from trains of four click stimuli. Three of these four stimuli are presented in one phase with the same amplitude and the fourth is presented in opposite phase at a level that is three times greater than each of the three previous stimuli. For each sweep, the ILO system distributes and stores the averaged response for one stimulus train into memory buffer A and the response for the other group of stimuli is stored in buffer B [59], and each buffer consists of 256 points. The final averaged waveform stored in A and B buffer is the averaged results of the total number of presentations. The usual clinical parameters of whole CEOAE response level and noise level (mPa) in the time domain were estimated and calculated using the root mean square (RMS) values provided by the ILO instrumentation from the averaged data in the two buffers. The CEOAE response level (mPa) is defined as the RMS

of the average of the corresponding A and B responses. The estimate of noise level (mPa) xN is defined as the RMS of the difference between the A and B responses [49]. Whole wave reproducibility is an important parameter which is often used clinically as a quality index of the recorded OAE. In the ILO system, whole wave reproducibility is calculated as the crosscorrelation coefficient between the averaged A(n) and B(n) waveforms (Eq. (4)), where n ¼1,2, y, T(256) indicates different samples. Whole wave reproducibility represents the similarity between the two memory buffer data sets during data collection. If the waveforms from the two buffers are quite similar, these particular waveforms are regarded as a true response, while the difference between the two waveforms is assumed to be noise. Generally, a CEOAE response is considered to be present when the whole wave reproducibility exceeds a threshold of 70% [59] P256 n ¼ 1 AðnÞBðnÞ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi The wave reproducibility g ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð4Þ P256 2 P256 2 A ðnÞ n¼1 n ¼ 1 B ðnÞ

Frequency analysis by ILO system The OAE frequency spectrum display was created using a fast Fourier transform (FFT). In the ILO system, the cross power spectrum of the A and B waveforms is considered as an estimate of CEOAE amplitude (Eq. (5)), and the auto power spectrum of the (A–B)/2 waveform represents an estimate of the noise level (Eq. (6)). The data can be analyzed using both a broadband display and a non-overlapping half-octave band mode which has been widely accepted for clinical work. In ILO V6 software, the center frequencies are set at 1, 1.4, 2, 2.8 and 4 kHz in half-octave band mode. The half-octave band calculation sums frequency regions 1/4 octave above and below the center frequencies. The SNR in each of the bands is defined as the difference between the signal and noise values (dB) at a specific frequency band P  o 9FFTðA=2ÞFFTðB=2Þ9 CEOAE amplitude ðdB SPLÞ ¼ 20log10 Pref ð5Þ P Noise level ðdB SPLÞ ¼ 20log10

o 9FFT½ðABÞ=29

Pref

! 2 ð6Þ

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Vicky Wei Zhang received a Bachelor’s degree in Biomedical Engineering from Capital University of Medical Sciences, Beijing, China, in 2001, and obtained her Ph.D. in Audiology from the University of Hong Kong in 2009. She is currently a research audiologist at the National Acoustic Laboratories, Australia. Her research interests focus on improved alternative techniques to conventional otoacoustic emission screening, hearing assessment and rehabilitation for infants and children.

Zhi-Guo Zhang received a B.Sc. degree in Electronic and Information Engineering from Tianjin University, Tianjin, China, in 2000, and he was awarded an M. Eng. degree in Electronic and Information Engineering Science from the University of Science and Technology of China, Hefei, China, in 2003. He started his Ph.D. degree in 2003 in the Department of Electrical and Electronic Engineering at the University of Hong Kong, and obtained a Ph.D. degree in 2008. His current research interests are in the general area of statistical signal processing and digital signal processing, and in particular adaptive filtering, nonparametric regression, image processing, robust statistics, time–frequency analysis and biomedical signal processing.

Bradley McPherson has worked as an audiologist for over twenty five years, in public hospitals and in private practice, as well as in university settings. He has worked in the United Kingdom, Australia, West and Southern Africa, East Asia, the Pacific and in the Middle East. Dr McPherson obtained his Ph.D. in Audiology from

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the University of Queensland, Brisbane, Australia and an M.Ed. in Audiology from the University of Manchester, United Kingdom. He is a professor at the University of Hong Kong, where he has held positions since 1996. Before coming to Hong Kong he was a Senior Lecturer in Audiology at the University of Queensland. At both Hong Kong and Queensland Universities his research has focused on effective screening techniques for hearing loss, in both adults and children.

Yong Hu received his B.Sc. and M.Sc. degrees in biomedical engineering from Tianjin University, Tianjin, China, in 1985 and 1988, respectively. He was awarded a Ph.D. degree from the University of Hong Kong, in 1999. He is currently an Assistant Professor in the Department of Orthopaedics and Traumatology, the University of Hong Kong. He is a distinguished adjunct professor at the Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union

Medical College. His research interests include neural engineering, clinical electrophysiology, biomedical signal measurement and processing.

Yeung-Sam Hung received his B.Sc. (Engineering) in Electrical Engineering and B.Sc. in Mathematics, both from the University of Hong Kong, and his M.Phil. and Ph.D. degrees from the University of Cambridge. He has worked as a research associate at the University of Cambridge, and as a lecturer at the University of Surrey before he joined the University of Hong Kong, where he is now a professor. He is a chartered engineer, a fellow of IET and HKIE, and a senior member of IEEE. He has authored and co-authored over 150 publications in books, journals and conferences. His research interests are in the areas of robust control and filtering theory, system modeling, robotics, computer vision, and more recently, biomedical engineering and machine learning techniques for bioinformatics.