PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Meniere's Disease Prognosis by Learning from Transient-Evoked
Otoacoustic Emission Signals
Sheng-Lun Kao1; Han-Wen Lien1; Tzu-Chi Liu1;ġHau-Tieng Wu2; Te-Yung Fang34; Pa-Chun Wang34;
Yi-Wen Liu1
1
2

Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan

Department of Mathematics and Department of Statistical Science, Duke University, North Carolina, USA
3

Department of Otolaryngology, Cathay General Hospital, Taipei, Taiwan
4School of Medicine, Fu Jen Catholic University, Taipei, Taiwan

ABSTRACT
Accurate prognosis of Meniere’s disease (MD) is difficult. The aim of this study is to treat it as a machinelearning problem through the analysis of transient-evoked (TE) otoacoustic emission (OAE) data obtained
from MD patients. Thirty-three patients who received treatment were recruited, and their distortion-product(DP) OAE, TEOAE, as well as pure-tone audiograms were taken longitudinally up to 6 months after being
diagnosed with MD. By hindsight, the patients were separated into two groups: those whose outer hair cell(OHC) functions eventually recovered, and those that did not. TEOAE signals between 2.5-20 ms weredimension-reduced via principal component analysis, and binary classification was performed via the support
vector machine. Through cross-validation, we demonstrate that the accuracy of prognosis can reach >80%
based on data obtained at the first visit. Further analysis also shows that the TEOAE group delay at 1k and2k Hz tend to be longer for the group of ears that eventually recovered their OHC functions. The group delay
can further be compared between the MD-affected ear and the opposite ear. The present results suggest thatTEOAE signals provide abundant information for the prognosis of MD and the information could be
extracted by applying machine-learning techniques.
Keywords: Otoacoustic Emission, Meniere’s Disease, Machine Learning, Signal Processing

1. INTRODUCTION
Since the discovery of otoacoustic emission (OAE) in 1978 (8), its presence has been utilized as a
non-invasive indicator of the status of the outer hair cells which amplify the inner ear vibration causedby sound waves. The OAE stimulated by short broadband acoustic impulses is referred to as thetransient evoked (TE) OAE. According to the characteristics of the cochlea, within 20 ms after thetransient click, the high-frequency of OAE is first transmitted then the low-frequency appears (7).
TEOAE and distortion product (DP) OAE tests have been widely used in clinical applications (9),such as classifying normal hearing and hearing loss by TEOAE long -latency components (11),classifying normal ears and MD ears by combining TEOAE and a masker tone of 30 Hz in a different
phase (13), newborn hearing screening based on TEOAE amplitude (19), and classifying MD-affected
ear and the opposite ear by TEOAE phase (12).
Few studies attempted hearing prognosis using TEOAE. Sudden sensorineural hearing loss (SSHL)
prognosis by TEOAE was proposed by Amiridavan M et al. (2). Lalaki et al. (10) and Shupak et al.
(17) have studied prognosis in idiopathic SSHL patients and concluded with a criterion that TEOAESNR≥ 3 dB would be an indicator for recovery of hearing. Overall, these studies suggested thatTEOAE may have prognostic values for hearing impairment of coch lear origins. To the best of ourknowledge, however, MD prognosis based on TEOAE signals has not been attempted.
MD is a cochlear dysfunction possibly due to endolymph swelling, and its symptoms include severe
vertigo, tinnitus, and acute hearing loss (23). MD usually affects only one ear and is more likely tohappen to adults of age 40 to 60 (23). In 2010, MD is estimated that 615000 cases diagnosed in the
United States and 45500 new cases occur each yearġ(23). Accurate prognosis of MD has been known
to be difficult (1) and finding a specific TEOAE pattern in MD patients has not been successful (4).

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This study focuses on MD prognosis and the aim is to find useful features from TEOAE to build apredicting model using machine learning techniques.
The rest of this paper is organized as follows. Data collection and steps of proposed approach aredescribed in Sec. 2. The machine learning model, results of classification, data visualizati on, andstatistical analysis are shown in Sec. 3. Finally, discussions and conclusions are given in Sec. 4 and
5, respectively.

2. Data collection and signal processing
According to the longitudinal pure-tone audiometry (PTA) (3) data of the MD-affected ears, we
manually label whether the patients had recover from hearing loss. Then, a predicting model was built
based on the TEOAE signals measured at the first visit. The general flowchart is shown in Figure 1
and details are described in Sec. 2.1 to 2.4.

Figure 1 – A block diagram of the proposed approach for machine learning-based prognosis. The
definition of the labels “Good” and “Bad” are given in Sec. 2.1.1.
2.1 Subjects
Thirty-three MD patients with unilateral MD-affected ear between age 20 and 65 were recruitedand their PTA and TEOAE were longitudinally tracked during the treatment period. For each patient,
after being diagnosed with MD, the PTA and TEOAE were measured every week (in week 1-4) in the
beginning; after that, the measurements were repeated in the 8th week, the 12th week (the third month),
and the sixth month. The PTA and TEOAE were obtained from both ears for every patient during
every follow-up visit. The Institutional Review Board of Cathay General Hospital approved this study(Approval Number. CGH-P104045).
The MD affected ears were grouped into two classes by comparing the PTA of the first and lastvisit according to the following criteria:z“Good” ears: if an improvement by 15 dB (e.g., the left panel of Figure 2) or more wasobserved in any of the PTA frequencies, or if the PTA results showed no improvement ofgreater than 15 dB in any frequency but the overall hearing could be categorized as normal(PTA ≤25 dB) or mild (26-40 dB) hearing loss.
z“Bad” ears: if ear has been diagnosed as MD chronic, or otherwise the PTA results showed
no improvement of greater than 15 dB (e.g., the right panel of Figure 2) in any frequency
while the hearing loss was categorized as moderate (PTA = 41-70 dB) to severe (>70 dB).
Following the definition above, we found 17 good cases (among which 15 ears had improved
hearing, and 2 did not) and 16 bad cases (4 MD Chronic, 12 no improvement in hearing). Data from32 non-MD affected ears were also collected.

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Figure 2 – Examples of pure-tone audiograms obtained longitudinally over 6 months. Left: a case of
possible OHC function recovery in the end. Right: a patient whose OHC function probably did notrecover. These two categories are arbitrarily named “Good” and “Bad” to refer to their OHC status.
2.2 Signal acquisition
The click stimulus was the same as described in (22). TEOAE signals were recorded by the ER10C (Etymotic Research Inc., Elkgrove Village, IL, USA) probe connected to a 24-bit UltraLite-mk3
Hybrid external sound card (MOTU, Cambridge, MA, USA). The transient click was generated bycomputer and transmitted through the soundcard to the ER-10C earphone. The sampling rate was set
to 44.1 kHz. The peak sound pressure level was about 74 dB relative to 20 uPa. The click rate wasmaintained at 10 per second. The TEOAE was recorded in a quiet oﬃce at Cathay General Hospital,
Taipei, and the noise ﬂoor was approximately 23-27 dB SPL. For each recording session, 3000 clicks
were played. Both the MD-affected ear and the opposite ear were measured.
2.3 Signal processing
This section describes the procedure of extracting TEOAE from the r aw click responses. The
process consists of the following steps.
2.3.1 Band-Pass filtering
Because the signals were not acquired in a soundbooth, power-line contamination at multiples of
60 Hz was observed with a peak at 120 Hz. To mend this problem, an 1.0-8.0 kHz bandpass filter was
designed using a Kaiser window (6) of length = 90 taps. The raw signals were convolved with theimpulse response of this filter in the time domain.
2.3.2 Artifact rejection
Large-amplitude acoustic noise (e.g., the sound of swallowing or the sound of friction due to body
movement) exists and it must be removed before analysis of TEOAE. Each recording session is cut
into 3000 frames so each frame contains one click. A frame is abandoned if any of the following
criteria is met:z
The amplitude of transient click is not between 1±0.1 times is the average amplitude of clicks
among all frames.
z
The instantaneous sound pressure of the frame exceeds 2 mPa at any moment. Note that,
empirically, human TEOAE amplitude in 2.5 to 20 ms after the click should not exceed 0.2
mPa or 20 dB SPL. Therefore, any noise at greater than 2 mPa is regarded as large deviationand should be discarded.
Also, as much as an entire file is concerned, if the total number of preserved frames is less than700, the session is regarded as a failure and the file would not be included in the dataset.
2.3.3 Median frame computing and noise-floor estimation
The TEOAE signal is estimated by computing the median among those frames that are preserved
after artifact rejection for every sample in the time domain. A schematic view of the procedure isshown in Figure 3, and the result is hereafter called the median frame. Also, to estimate the signal tonoise ratio (SNR) by TEOAE repeatability (16), a noise-floor signal is first defined as follows,݂௡௢௜௦௘ ሺ݅ሻ ൌ݂ଵ ሺ݅ሻ െ ݂ଶ ሺ݅ሻ ൅ ‫ ڮ‬൅ ሺെͳሻ௡ିଵ ݂௡ ሺ݅ሻ
ǡ
݊

(1)

where ݂ଵ ǡ ݂ଶ ǡ ǥ ǡ ݂௡ denote individual frames, ݅ denotes the time index, and ݊ denotes the total
number of preserved frames after artifact rejection. An example of the estimated TEOAE signal (i.e.,the median frame) and the corresponding ݂௡௢௜௦௘ ሺ݅ሻ for that session are shown in Figure 4. The power
spectrum of this noise-floor signal enables calculation of SNR at every frequency, as shown in Figure
5.

Figure 3 – A schematic illustration of computing the median frame; ݊ denotes the number of6507

preserved frames after artifact rejection, and ݉ is the length of each frame.

Figure 4 – An Example of the estimated TEOAE signal (blue) and the noise-floor signal (red).
2.4 Feature extraction
This section describes the procedure of transferring the TEOAE frame (2.3.4) to features. The
process consists of four steps listed as follows.
2.4.1 TEOAE after principal component analysis (PCA)
After denoising the signal via taking median, an application of PCA for dimension reduction iscarried out (21). The TEOAE signals in 2.5-20 ms after the click are analyzed and dimension equals
to the number of samples, which is 772. In order to avoid overfitting in the subsequent supervised
learning procedures, the dimensionality is reduced to three by PCA (14)ġfirst.
PCA is a statistical method of unsupervised learning which simplifies the dataset at hands. PCA isoften used to reduce the dimensionality of data while maintaining the features that contribute the mostin the data set in terms of the variance.
In this research, the dataset is denoted as a matrix  ெൈே , where ‫ ܯ‬is the total number of ears, andܰ ൌ ͹͹ʹ is the total number of samples in each TEOAE signal. First, the covariance matrixġ ۱ሺሻேൈே
is calculated as follows:۱ሺሻ ൌ ‫ܧ‬ሾሺܺ െ ‫ܧ‬ሾܺሿሻሺܺ െ ‫ܧ‬ሾܺሿሻ் ሿǡ
(2)where the expectation is taken along the ear dimension. Then, the eigenvalue (λ) and the corresponding
eigenvector (w) of are calculated by solving the following system of equations,(3)ሺ۱ሺሻ െ ߣ‫ܫ‬ሻ‫ ݓ‬ൌ ૙ǤFinally, the eigenvalues are sorted in the descending order, and the eigenvector corresponding to
the first ‫ ܭ‬ൌ ͵ eigenvalues are selected as the eigenvector set ே௫௄ for projection. The features after
dimension reduction, Ԣெ௫௄ , is defined as follows,ܺԢெൈ௄ ൌ ܺெൈே ൈ ܻேൈ௄ Ǥ
(4)Each column in the matrix Ԣெ௫௄ is considered a feature and the values are hereafter called
TEOAE-after-PCA.
2.4.2 SNR
The signal-to-noise ratio (SNR) captures the relative strength of the TEOAE signal with thebackground noise. In this study, we attempt to evaluate its usage as a feature for machine prognosis
purposes. The SNR is defined as follows,‫ ܞܞ‬೅ ൌ ͳͲ ଵ଴ ቀ‫ܝܝ‬೅ ቁǡ (5)where ‫ ܞ‬and ‫ ܝ‬are row-vector representations for the TEOAE signal and the noise-floor signal,
respectively for each recording session, respectively.
2.4.3 Energy
Although the information might be somewhat redundant, to evaluate its usefulness for machine
prognosis purposes, the absolute energy is also calculated,(6)‫ܧ‬௦ ൌ  ‫ ܶܞܞ‬Ǥ
2.4.4 Group delay
The group delay ߬ሺ߱ሻ is the energy delay time of the signals in different frequencies. It can becalculated as follows:

6508߬ሺ߱ሻ ൌ െ

ௗ
ௗఠ

(7)

ሾ‫ܺס‬ሺ݁ ௝ఠ ሻሿǡ௝ఠwhere ‫ܺס‬൫݁ ൯ġ denotes the phase spectrum of the TEOAE signal. An example of ߬ሺ߱ሻ of an ear thatwas not affected by MD is shown in Figure 5. Empirically, since MD is known to affect the lowfrequency hearing (5) more than the high frequency hearing, we selected the group delay at 1 and 2
kHz as potentially useful features for machine prognosis.
In practice, the phase spectrum of a signal would be less reliable at frequencies with low SNR ; forexample, in the left panel of Figure 5, noise magnitude is close or even larger than signal magnitude
at some points. To avoid large errors in subsequent calculation of the group delay, a point pickprocedure was adopted. If any frequency has an SNR of less than 2 dB, that frequency bin would beabandoned. In order to get continuous phase, the lowess smoothing with length 150 points, about 3.2
kHz, was applied, and the phase curve after smoothing was shown in Figure 5 (right panel).

Figure 5 – An Example of the TEOAE magnitude spectrum (left), the corresponding group delay(middle) and the phase spectrum (right). Note that both the phase spectrum before and after smoothing
are shown for comparison.

3. Machine learning results of prognosis
A support vector machine (SVM)ġ(20) classifier was trained to predict whether an MD-affected ear
would turn out to be a “good” case or a “bad” case (See Sec. 2.1 for the definition). The SVM classifier
takes the features listed in Sec. 2 (e.g., TEOAE after PCA, SNR, group delay at 1 and 2 kHz) as the
input, acquired during the patient’s first visit to the clinic after the onset of initial MD symptoms. The
machine learning methods and evaluations are given in 3.1, the features of TEOAE are visualized in
3.2, and some additional statistical analysis is given in 3.3.
3.1 Classification by SVM
First, the value of each feature is normalized so the range becomes between 0 (the minimum among
all ears) and 1 (the maximum). This normalization step is necessary so the SVM training would notfavor one feature over the other simply because of the choice of unit. The adopted SVM library isfrom Scikit-Learn (15), and the best penalty parameter and kernel coefficient were determined bycross validation.
Empirically, we chose the sigmoid function as the kernel function for SVM. The prognosis
accuracies of different feature combinations with 3, 4, 5, or 16-fold cross validation are shown in
Table 1. In particular, the confusion matrix of 4 cross-validation using TEOAE-after-PCA and groupdelay is shown in Table 2.
Observing the accuracy in Table 1, note that the combination of TEOAE-after-PCA and group delay
produces the best results; the results do not differ by more than 10% between 3, 4, 5, and 16-fold crossvalidation.
Table 1 – Prognosis accuracy (%) based on different feature combination
Features3 cross-

4 cross-

5 cross-

16 cross-

validation

validation

validation

validation

TEOAE-after-PCA

81.81

78.125

80.00

81.25

SNR

60.60

65.625

73.33

62.50

Energy54.54

71.875

66.66

62.50

Group delay

75.75

78.125

76.66

78.13

TEOAE-after-PCA & Group delay

84.84

90.625

86.66

81.25

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Table 2 – The confusion matrix of 4 cross-validations by combining group delay at two frequencies
and TEOAE projections onto three principal components
Predicted
Predicted
M=32“Good”“Bad”

Actual: “Good”

13

3

Actual: “Bad”

016

3.2 PCA data visualization
After PCA, the dimension of TEOAE data is reduced from 772 to 3 and it can be visualized b y 3D plots as shown in Figure 6.

Figure 6 – (3 cases) TEOAE data reduced to three dimensions after PCA
The eigenvectors corresponding to three largest principal components are shown in Figure 7. By
inspection, note that the first two components seem to capture the middle -ear ringing more than thethird component, and the third component looks more reminiscent of an actual TEOAE signal.
However, in Figure 6 it is hard to say whether the “good” and “bad” ears tend to segregate better along
one of the PCA axes. Further analysis on the distribution of each feature is given next.

Figure 7 – (Left to Right) Eigenvectors of the largest three principal components
3.3 Statistical analysis for each feature
The difference in the statistical distribution between “good” and “bad” ears along each feature
dimension is quantified by the Welch’s t-test (18), and the results are shown in Table 3. Note that the6510

difference in group delay was the most significant (with the smallest p values). Also note that no
significant difference was found in SNR, and energy features. This argues against the traditional
wisdom of treating the magnitude of TEOAE per se as an indicator for inferring the cochlear status.
Table 3 – Statistical analysis by Welch’s t-test based on different feature
Features

Test Statistic

p-value

TEOAE after PCA1

2.30890.0291TEOAE after PCA2

-0.2732

0.7869TEOAE after PCA3

1.7692

0.0926

SNR-0.0926

0.9268Energy0.81600.4212

Group delay_1k2.94090.0066

Group delay_2k

3.91270.0008

4. Discussion
This study focuses on finding useful features for MD prognosis purposes. By extracting featuresfrom TEOAE waveforms, we found that the combination of TEOAE-after-PCA and TEOAE groupdelay at 1 and 2 kHz give the best prediction accuracy. The present results suggest that TEOAE
waveforms might contain useful information for inferring the cochlear conditions in MD affected ears.
For the first and second eigenvectors shown in Figure 7, the instantaneous frequency at the
beginning seems to be higher than toward the end, which is consistent with the characteristics of
TEOAE (7). In the future, if more data are available, more principal components could be visualized
and perhaps useful information could be unraveled.
Presently, we encounter low SNR at 4 and 8 kHz due to relatively strong middle -ear ringing at thetime course when high frequency (>4 kHz) components are supposed to be emitted from the cochlea.
Therefore, though not shown in this article, the group delay calculated at 4 and 8 kHz turned out tobe not helpful for SVM-based classification, because the group delay value was mostly close to 3
milliseconds in each case. To extract reliable signal at high frequencies, a nonlinear paradigm forsignal acquisition might be considered. Nevertheless, since MD is known mostly to affect low
frequency hearing rather than high frequency (5), chances are that 1 and 2 kHz components are still
more reliable than higher frequencies for inferring cochlear conditions in MD-affected ears. For the
prognosis of other acute hearing loss of cochlear origins, however, acquiring features from a broader
frequency range might be preferred. In the future, frequency components below 1.0 kHz should also
be analyzed if SNR could be improved and the power-line noise encountered in the present study canbe physically blocked.

5. CONCLUSIONS
Useful features in TEOAE have been discovered for MD prognosis purposes. Combining TEOAE
waveforms in 2.5~20ms and TEOAE group delay at 1 and 2 kHz gives the best prediction accuracy.
Results of this study support our research motivation that the doctor could know if an MD patient's
hearing had any chance to improve based upon examination during the first visit. The present results
suggest that TEOAE signals provide abundant information for the prognosis of MD and theinformation could be extracted via machine-learning techniques.

ACKNOWLEDGEMENTS
This research is supported by the Ministry of Science and Technology, Taiwan, under Grant No.
107-2221-E-007-093-MY2.

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