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

A clinical test battery for Better hEAring Rehabilitation (BEAR):
Towards the prediction of individual auditory deficits and hearingaid benefit
Raul H SANCHEZ-LOPEZ1, Silje Grini NIELSEN1, Oscar CAÑETE1, Michal FERECZKOWSKI1,
Mengfan WU2, Tobias NEHER2, Torsten DAU1 & Sébastien SANTURETTE1,3.
1

Hearing Systems Section, Dept. of Health Technology, Technical University of Denmark, Kgs. Lyngby,
Denmark2

Institute of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
3

Oticon A/S, Smørum, Denmark

ABSTRACT
One aim of the Better hEAring Rehabilitation (BEAR) project is to define a new clinical profiling tool, a test
battery, for individualized hearing loss characterization. Recently, Sanchez-Lopez et al. (2019) proposed atest battery that includes six types of measures: audibility, middle-ear analysis, speech perception, binauralprocessing abilities, loudness perception, and spectro-temporal resolution. The results of 75 listeners wereanalyzed using a data-driven approach (Sanchez-Lopez et al., 2018), which provided evidence for the
existence of two independent sources of distortion and four different auditory profiles. The classification of
the listeners into auditory profiles allows the prediction of the performance of the listeners on different
psychoacoustic tasks as well as their expected aided speech intelligibility. For clinical practice, a decision
tree with a small set of highly predictive tests is desirable for an efficient classification of hearing-impaired
individuals. The main aim of the present study was to investigate the optimal decision tree and to propose aclinically feasible test battery with a minimum number of tests for accurate listener classification. The clinicaltest battery will be used in a large-scale field study that will help implement a hearing-aid fitting protocol for
better hearing rehabilitation.
Keywords: Audiology, data-driven, supra-threshold distortions

1

INTRODUCTION

The Better hEAring Rehabilitation (BEAR) project pursues the development and implementationof new methods for the diagnosis of hearing deficits as well as new hearing-aid compensation
strategies to improve hearing rehabilitation. Since digital hearing aids were introduced to the market,
hearing-aid users have reported increased benefit (1), probably because of the advanced signal
processing techniques (or features) that are now commonly available, such as directionality, noise
reduction and dynamic range compression. However, the hearing-aid fitting is still based on theaudiogram only which provides the basis for frequency-dependent gain prescription. The otherfeatures are adjusted based on preferences and not according to the individual auditory deficits of theuser. Furthermore, in hearing care clinics it is common to “fine-tune” some hearing-aid parameters
during follow-up visits (2). If the initial fitting is near-optimal, the follow-up visit may focus on
individualization of the fitting parameters according to the “life-style” of the patient. However, if theinitial fitting is far from optimal, the audiologist needs to tailor-fit hearing-aid parameters to thehearing deficits of the listener by “trial-and-error”. The BEAR project attempts to improve thissituation by identifying groups of listeners – or “auditory profiles” – with specific performancepatterns on a range of threshold and supra-threshold tasks and by providing tailored solutions withproposed dedicated hearing-aid compensation strategies for each auditory profile.
In an attempt to identify the auditory profiles, Sanchez-Lopez et al. (3) hypothesized that thehearing deficits of a given listener can be described as the combination of two independent types ofauditory distortions. The hypothesis was based on the idea that each type of distortion can cause boththreshold and supra-threshold deficits and that these deficits are not necessarily independent. In Figure

38411.a), the two types of distortions create a two-dimensional space where a given listener ’s location isdetermined by the degree of severity of these distortions. As a result, the listener can be identified as
belonging to a certain auditory profile. As shown in Figure 1, normal-hearing listeners are located at
the bottom left-hand corner, exhibiting no distortions. Profile A corresponds to a group with minor
distortions and therefore good performance in general. Profile C exhibits a high degree of both typesof distortions. Profile B exhibits a high degree of distortion type I. Profile D shows a high deg ree ofdistortion type II. Using a data-driven approach (3), four auditory profiles were identified by analyzing
the data from two previous studies, providing evidence for the validity of this approach. However, thesubstantial differences in terms of listeners and tests applied in these two studies limited the overall
conclusions that could be drawn from this work.

Figure 1 – Hypothesized auditory profiles together with the results of the data-driven profile identification.
Left panel: The listeners are placed in a two-dimensional space along two dimensions of auditory
distortion. Right panel: Using a data-driven analysis, listeners are placed in the two-dimensional space as afunction of their probability of belonging to a specific profile.

In order to overcome the aforementioned limitations discussed in (3), a new test battery including
a range of supra-threshold psychoacoustic tests was proposed and evaluated in 75 listeners withvarious types of audiometric configurations. Additionally, the test-retest reliability of the new test
battery was investigated in a subset of 11 listeners (4). The dataset obtained in this manner will in thefollowing be referred to as BEAR 3. For the classification of the 75 listeners, unsupervised learningtechniques were used to carry out iterative auditory profiling based on the data-driven approach (5).
After this analysis, 70 of the 75 listeners were reliably identified as belonging to one of the four
auditory profiles A-D and the remaining five listeners (shown in grey in Figure 1) were left
unclassified. However, this iterative unsupervised method requires the entire dataset to identify thefour groups and is therefore not suitable for the classification of new listeners. Decision trees are awell-known simple classification tool that may prove useful for classifying unseen data, i.e. newlisteners. The efficacy of decision trees can be explored by evaluating their classification performance(6).
When implementing a new protocol for diagnosing a specific disease in the clinic, it is crucial toevaluate its ability to correctly identify the patients who are affected by the health problem underconsideration. In general, two types of errors can occur in this classification process: truly affectedpatients may be “missed” (false negatives) and healthy patients may be “misclassified” as being
affected (false positives). Confusion matrices are typically used to quantify the test performance of aclassifier. In addition to the classification performance, it is of interest to investigate the cost
efficiency of a new clinical protocol (7) by estimating the cost of having false negatives or false
positives as well as the benefit that the correct classification would provide.
The goal of the current study was to develop a decision tree for a large field study to be conducted
as part of the BEAR project, where listeners will have to be classified into the four hypothesized
auditory profiles. It is also of interest to identify an additional group of unclassified listeners (Uc)
who do not seem to belong to any of the four primary profiles. The BEAR3 dataset was used for3842

investigating the accuracy and efficiency of different decision trees. Using supervised learningtechniques, different classification strategies were tested and evaluated in terms of both test
performance and cost effectiveness.

2

Methods

Decision tree classifiers were trained for predicting the identified auditory profiles from (5) using
supervised learning. The analysis of the cost efficiency was based on considerations made in thecontext of the aforementioned field study to be carried out in different hearing clinics. These
considerations cover the recruitment of a random sample of 500 listeners where at least 60 listeners
per profile are expected.
2.1 Classification methods
The classification of the listeners into the four auditory profiles was performed using supervised
learning with tests that showed good to excellent reliability as input, and the labels of the four auditory
profiles as well as the unclassified group (Uc) as output. The classification algorithm used here was astandard classification and regression tree (CART), which makes use of recursive binary partitions inorder to fit the data to the best set of binary decisions or splits (8). Four classification schemes wereconsidered:xxxx

DTA: A simple classification based only on the audiogram.
DT10: A multi-label “fitted” classification. Decision tree based on all reliable tests and 10
binary decisions.
DT7: A multi-label “pruned” classification with seven binary decisions.
DT4: A multi-label “pruned” classification with four binary decisions.

Figure 2 – Top panel: The complete decision tree (DT10). Bottom panel: The four decision trees consideredin the present study. DTA: Audiometry-based classifier, DT10: Same as in top panel; DT7: Decision treewith seven binary decisions (DT10 with three pruned splits), and DT4: Decision tree with four binary
decisions (DT10 with six pruned splits).
Figure 2 illustrates the complete classification tree (DT10). Each diamond (split) corresponds to alogic rule related to a given variable, for example HLHF > 45 dB HL. The right branch corresponds topoorer outcome and the left branch to better outcome. The details of the logic rules are not shown
here. The decision trees DT7 and DT4 are the result of pruning the decision tree DT10 by discardingsome of the nodes, as illustrated in the figure.

3843

2.2 Test performance and cost-efficiency evaluation
In order to evaluate both the classification performance and the cost efficiency, a dataset was
created for bootstrapping. The original dataset was copied seven times which resulted in 525
observations. Next, the specific standard error of the measurements (SEM) of each test (4) was used
for introducing some uncertainty (additive Gaussian noise) in the outcomes to simulate the data fromthe aforementioned field study. Confusion matrixes were then calculated for 100 iterations and thecost-efficiency was estimated.
The cost-efficiency was calculated based on (7) and adapted for the multi-label case. Consider a
2x2 matrix of costs C. Following the previous assumptions, C 00 is the cost of a true negative, i.e. aparticipant to be excluded from the study or correctly “not-classified” as a given profile. The cost C00
would be equal to the session cost. C 11 is the cost of a correct classification. C 01 and C 10 correspondto false positives and false negatives, respectively, which would introduce outliers in the final results.
These would be equal to the cost of misclassification. Additionally, consider the matrix P with theprobabilities of each of the previous cases, where P 11 is the probability of correct classification, P 00
that of correct rejection, and P 01 and P 10 those of the two types of misclassification. The expected costis the Hadamard product of the P and C matrixes:‫ ݐݏ݋ܥ݀݁ݐܿ݁݌ݔܧ‬ൌ ‫۾‬ι۱(1)This generic expression can then be simplified due to the fact that the probability of belonging toa given class ݈ܿܽ‫ݏݏ‬௜ not truly belonging to that group ܲሺ݈ܿܽ‫ݏݏ‬௜ ȁ݈ܿܽ‫ݏݏ‬௝ ሻ is equal to ͳ െܲሺ݈ܿܽ‫ݏݏ‬௝ ȁ݈ܿܽ‫ݏݏ‬௝ ሻ. The index i denotes the predicted class and the index j the actual class. Therefore,
the expression can be simplified to:‫ ݐݏ݋ܥ݀݁ݐܿ݁݌ݔܧ‬ൌ ෍ ෍ ܲ൫݈ܿܽ‫ݏݏ‬௜ ห݈ܿܽ‫ݏݏ‬௝ ൯ െ Ⱦܲሺ݈ܿܽ‫ݏݏ‬௝ ȁ݈ܿܽ‫ݏݏ‬௝ ሻǡ
௜

(2)

௝

where E is defined asߚൌ‫ܥ‬ଵଵ െ ‫ܥ‬଴ଵ ܲሺ݈ܿܽ‫ݏݏ‬௝ ሻǤ‫ܥ‬଴଴ െ ‫ܥ‬ଵ଴ ͳ െ ܲሺ݈ܿܽ‫ݏݏ‬௝ ሻ(3)Given that the probabilities can be calculated in terms of the specificity and sensitivity, Equation 2
can be written as follows:‫ ݐݏ݋ܥ݀݁ݐܿ݁݌ݔܧ‬ൌ ෍ ܵ‫ݕݐ݂݅ܿ݅݅ܿ݁݌‬௝ െ Ⱦܵ݁݊‫ݕݐ݅ݒ݅ݐ݅ݏ‬௝ Ǥ

(4)௝

Equation 4 provides the extension of the expected cost as defined in (7) but for the multi-class case.
It was used here to calculate the expected cost for each iteration to estimate the final expected cost
per session.
2.2.1 Cost assumptions
The following assumptions were made:x Test cost: Each additional test that is not part of current clinical practice incurs costs forthe implementation, the training of the examiners and the documentation. Uus et al. (9)analyzed the costs of implementing a newborn screening program. The average set-up costacross 16 sites for implementing two tests was £665 for 1000 infants. Therefore, in thepresent study, a hypothetical total cost of $600 was considered for a field study thatinvolves 500 listeners. The cost per new test per session would therefore be $1.2.
x Session cost: The duration of the session has a cost that involves the salary of the examiner
and the use of the facilities. Taking the average of the costs suggested in (10–12) andassuming that one session lasts for one hour, this leads to a hypothetical cost of $60 per
session or $1 per minute.
x Correct classification: The correct classification of a given listener increases theprobability for the study to be successful. As suggested in (7), this should involve the longterm benefits, including the future reduction of follow-up visits in the clinics if the projectis a success. In this case, we limited the expected benefit to the reduction of follow-up

3844

x

visits. Tecca (13) recently studied the number of visits and the incidence of hearing-aid
fitting-related complaints during the first six weeks of hearing aid use. It was shown thatthe first and second visit involved changes in the gain and advanced features in more than
70% of the cases. Since the new BEAR fitting rationale aims to provide a better first-fit
solution, a cost of one follow-up visit ($60) was considered here.
Misclassification cost: The cost of classifying the listener as belonging to a different
auditory profile would correspond to the extra efforts used for this listener to obtain theiroptimal fitting, i.e. follow-up visits. Here, it is assumed that these listeners would beunsatisfied with their fittings because that corresponds to any other auditory profile andthat this would lead to two extra follow-up visits for fine-tuning and verification ($120).

Table 1 shows the characteristics of each of the considered classifiers in terms of number of tests, theduration of the session and the total test cost and session cost.
Table 1- Description of the four classifiers in terms of number of tests, duration and costs. The number oftests includes the outcome measure HINT by default even in the case of DTA and DT4 where this test is notpart of the decision trees.

3

Decision
Trees

Description

Number of
tests

Duration(min)

Test cost ($)

Session cost
($)

DTA

Audiometry classifier

1

27

1.2

28.2

DT10

Complete classifier

5

68

6

74

DT7

Pruned classifier I

4

41

4.8

45.8

DT4

Pruned classifier II

3

34

3.6

37.6

Results

3.1 Classification performance
The four classifiers were tested with a constructed data set based on the original data of the BEAR 3
data after applying bootstrapping. Figure 3 shows the confusion matrices of the four classifiers.

Figure 3 – Confusion matrices corresponding to one single iteration of classification for each of the four
tested decision trees.

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The audiometry-based classifier (DTA) was able to correctly predict 67% of the data with a low
sensitivity in the predictions of profiles B and C and a low specificity in the case of profile D (19%).
The classifiers DT10, DT7 and DT4 had an overall accuracy of 85%. However, they differed in termsof the specificity and sensitivity for each of the profiles. DT10 and DT7 were able to identify some
Uc listeners correctly. In contrast, while DT4 lead to a higher specificity in the classification of profile
D, it had the disadvantage that all the Uc listeners would be classified as any of the four profiles. The
main difference between DT10 and DT7 was the sensitivity, especially for the Uc listeners. DT10 was
more accurate for the Uc listeners, and it had also higher specificity for some of the other profiles.
DT7 predicted less Uc listeners and misclassified more true B listeners, but had also higher sensitivity
for profile C. Overall, the decision trees that contain binary decisions for identifying Uc listeners(DT10 and DT7) were both more accurate and more specific.
3.2 Expected cost
The expected cost was calculated according to Equation 4. Additionally, the total expected cost of
the field study was estimated by the sum of the fixed costs, the planned sessions for 500 listeners
recruited randomly, and the additional sessions needed for fulfilling the requirement of testing 60
listeners in each profile.
Figure 4 shows the expected costs. The left panel illustrates the differences among the four decision
trees where DTA resulted in higher costs than the other three classifiers. DT10 provided higher costefficiency with $4 gained per listener, followed by DT7 and DT4 with $1.5 each per listener. The rightpanel of Figure 4 shows the total cost of the field study. The audiometry-based DTA classifier was theone with the lowest fixed and planned costs but the highest total cost. This is because of the risk of
misclassification, which requires numerous additional listeners to get 60 subjects in profile D, with a
total of 1992 listeners. DT10 and DT7 required a similar number of listeners (~675 listeners in total)but differed in the session cost, making DT7 a cheaper decision tree overall. The last decision tree
DT4 provided the lowest total cost and required a lower number of additional measurements (564listeners in total). However, the disadvantage is that DT4 cannot identify Uc listeners and would
therefore classify them as belonging to one of the four auditory profiles. If the aim is to achieve a low
number of misclassified listeners, this classifier would not be the optimal choice.

Figure 4 – Expected classification cost per session and total classification cost. The total classification cost
involves the costs of the implementation of new tests (Fixed), the session costs for 500 listeners(Sessions500) and the cost of additional sessions (SessionsAdd).
Overall, the results suggest that DT10 would be the best candidate for the considered field study,
due to its higher sensitivity and specificity. Moreover, the clinical test battery that can help to better
define the auditory profiles in a larger population by gathering information related to auditory spectrotemporal resolution, speech intelligibility, loudness perception and binaural processing abilities.

3846

4

Discussion

The results of the present study speak in favor of a reduced test battery based on five tests included
in DT10 for classifying listeners in clinical practice. These tests include adaptive categorical loudness
scaling (ACALOS), the hearing in noise test (HINT), the binaural pitch (BP) test, the frequency
threshold for identifying interaural phase differences (IPD), and a fast version of the spectro-temporalmodulation sensitivity test (fSTM). As such, the proposed clinical version of the test battery covers
four domains: loudness perception, speech-in-noise intelligibility, binaural processing abilities andspectro-temporal resolution. Although the original BEAR test battery also involved tests related to
audibility and middle-ear analysis (as well as some additional tests in the four covered domains), thefive tests were found to be the most informative and reliable for the classification of the listeners inauditory profiles.
The ACALOS test is able to estimate hearing thresholds, which are comparable to the ones
provided by pure-tone audiometry (14). In the present study, these estimates were used as the mostinformative predictors for the fitted classifier (DT10) instead of the audiometric thresholds. ACALOS
is also able to provide supra-threshold information related to loudness perception, such as the slopeof the growth of loudness, the most comfortable level and the overall dynamic range. Therefore, theuse of ACALOS could be of interest not only for the purpose of auditory profiling but also for hearingaid fitting. For example, it would provide information about the growth of loudness of the patient that
could guide fine-tuning of the gain at different input levels. Moreover, fitting formulas based on
loudness normalization could be refined if loudness is measured with this technique (15).
The results of the fSTM test showed that profile C listeners have significantly poorer performance
than listeners belonging to profiles A, B or D. This makes this new test quite interesting forclassification. Additionally, the HINT results showed that profile B and C listeners had elevatedspeech reception thresholds in noise, suggesting that hearing-aid outcome will improve for theselisteners if advanced processing is able to increase effectively improve the signal-to-noise ratio.
Therefore, it would be interesting to investigate whether the tests involved in DT4 only (ACALOS,fSTM) are sufficient for a short version of the clinical test battery. Moreover, these tests are notlanguage-dependent, in contrast to HINT. Although DT4 could be more easily adopted by the publichealth centers due to the cost-efficiency and shorter duration of the tests (35 min), the use of more
informative tests, including the complete decision tree (DT10), should be of higher priority for a fieldstudy with research as the main purpose.
The unclassified group can only be identified using DT10 or DT7. It is of interest to identify thisgroup during the field study further explore their supra-threshold auditory performance, which could
help better understand the consequences of hearing loss in those listeners.

5

Conclusion

The results of the present study support the implementation of new audiological tests in the clinic
to achieve a more comprehensive definition of the hearing abilities of patients with hearing loss. Fourdecision trees were evaluated in terms of classification performance an d cost efficiency. The mostinformative and reliable tests beyond the audiogram were found to include the evaluation of spectrotemporal modulation sensitivity, loudness perception and binaural processing abilities . The BEAR
clinical test battery will be evaluated in a large-scale study together with the new profile-basedhearing-aid fitting strategies. The BEAR clinical test battery based on DT10, and proposed for such afield study, is available in a public repository 1.

ACKNOWLEDGEMENTS
We want to thank Dorte Hammershøi, James Harte and the other BEAR partners for their input
during the realization of this study. This work was supported by Innovation Fund Denmark Grand
Solutions 5164-00011B (Better hEAring Rehabilitation project), Oticon A/S, GN Hearing, Widex A/S,
Aalborg University, University of Southern Denmark, the Technical University of Denmark, Force
Technology, and the university hospitals in Aalborg, Odense and Copenhagen. The funding and input
1

https://bitbucket.org/hea-dtu/bear-test-battery/3847

from all partners is sincerely acknowledged.

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