Methods and systems for auditory brainstem response analysis

Abstract

Disclosed are methods including receiving, at a computing device, for at least two intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples; generating, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of cross-correlation samples generated by iterative bootstrap sample signal processing of the individual plurality of ABR waveform samples; and calculating, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution; and identifying an ABR threshold intensity of the plurality of intensities by comparing the individual mean for the at least two intensities of the plurality of intensities to a threshold criterion.

Description

METHODS AND SYSTEMS FOR AUDITORY BRAINSTEM RESPONSE ANALYSISCROSS REFERENCE TO RELATED PATENT APPLICATION

[0001] This application claims priority to U.S. Provisional Application No. 63 / 549,024 filed February 2, 2024, herein incorporated by reference in its entirety.BACKGROUND

[0002] Hearing or auditory tests may be conducted on humans and animals for various reasons, including detecting hearing damage / loss or hearing sensitivity in a test subject, testing efficacy of hearing loss interventions, gathering baseline hearing performance data, etc. The various types of hearing or auditory tests that may be administered may be selected based on the type of analysis being conducted and an ability of the subject to participate in the test. For example, a standard, pure-tone auditory test may be performed on a subject capable of responding to verbal commands to detect and indicate to a tester presence of a particular tone. Other types of auditory testing may be performed that rely on physical observation of the subject. However, for many types of auditory testing where the subject is incapable of actively participating in the test by providing feedback, determining results can be time consuming and somewhat subjective. Overall, there is a need for more robust and auditory test result determination models.BRIEF SUMMARY

[0003] This application describes methods for determination of auditory threshold using results of an auditory brainstem response (ABR) test. An ABR test (e.g., also described as measuring an auditory evoked potential) may provide an indication as to how the inner ear (e.g., the cochlea), and the brain pathways for hearing, are working. An ABR test may be performed by emitting an audio stimulus signal (e.g., a tone or tones at a particular frequency or series of frequencies) at different intensities (e.g., amplitudes or sound levels) through a transducer-containing device (e.g., headphones or other speaker), and measuring auditory neural response activity via electrodes placed on a head of a subject to detect responses to the emitted tones. The electrodes may be coupled to a special-purpose computer. During an ABR test, the audio stimulus signal may be repeated many times for each sound intensity to gather many corresponding ABR waveform samples.

[0004] The ABR waveform samples corresponding to at least two sound intensities of the sound intensities may be analyzed to determine an ABR threshold intensity. The ABR threshold intensity may indicate a minimum sound intensity hearing level for a subject being tested. For each of the at least two sound intensities, the analysis may include statistical bootstrapping, which may include several iterations of 1) randomly separating the ABR waveform samples into two groups, 2) calculating a median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution for the many iterations for a given sound intensity may be analyzed to determine a mean. The mean crosscorrelation values for the at least two sound intensities may be fit to a sigmoid or powerlaw curve to determine the ABR threshold intensity. The statistical analysis process using statistical bootstrapping and the mean sound intensity of the cross-correlation distribution may provide more accurate and reliable results as compared with other ABR analysis techniques.

[0005] Additional advantages of the disclosed method and compositions will be set forth in part in the description which follows, and in part will be understood from the description, or may be learned by practice of the disclosed method and compositions. The advantages of the disclosed method and compositions will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several embodiments of the disclosed method and compositions and together with the description, serve to explain the principles of the disclosed method and compositions.

[0007] FIG. 1 is a schematic block diagram of an ABR test system, in accordance with embodiments described herein.

[0008] FIG. 2 is a schematic block diagram of an ABR test system, in accordance with embodiments described herein.

[0009] FIG. 3 is flowchart of a method to determine an ABR threshold from ABR waveform samples, in accordance with embodiments described herein.

[0010] FIGs. 4A and 4B illustrate example signal plots for an ABR test analysis, in accordance with embodiments described herein.

[0011] FIG. 5 is flowchart of a method to determine an ABR threshold from ABR waveform samples, in accordance with embodiments described herein.

[0012] FIG. 6 is flowchart of a method to determine an ABR threshold from ABR waveform samples, in accordance with embodiments described herein.

[0013] FIGs. 7-9 include various exemplary illustrations of experimental data using the ABR threshold intensity classification methods described herein.DETAILED DESCRIPTION

[0014] The disclosed systems, devices, apparatuses, and methods may be understood more readily by reference to the following detailed description of particular embodiments and the Example included therein and to the Figures and their previous and following description.

[0015] It is understood that the disclosed systems, devices, apparatuses, and methods are not limited to the particular methodology, protocols, and reagents described as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention which will be limited only by the appended claims.

[0016] It must be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural reference unless the context clearly dictates otherwise. Thus, for example, reference to “an antibody” includes a plurality of such antibodies, reference to “the antibody” is a reference to one or more antibodies and equivalents thereof known to those skilled in the art, and so forth.

[0017] Throughout the description and claims of this specification, the word “comprise” and variations of the word, such as “comprising” and “comprises,” means “including but not limited to,” and is not intended to exclude, for example, other additives, components, integers or steps. In particular, in methods stated as comprising one or more steps or operations it is specifically contemplated that each step comprises what is listed (unless that step includes a limiting term such as “consisting of’), meaningthat each step is not intended to exclude, for example, other additives, components, integers or steps that are not listed in the step.

[0018] FIG. 1 is a schematic block diagram of an ABR test system 100, in accordance with embodiments described herein. The system 100 includes an ABR test system 120 coupled to an electrode 110 via a conductor 111, an electrode 112 via a conductor 113, and a transducer 114 via a conductor 115. The ABR test system 120 may perform an ABR test on a subject 102 (e.g., human (e.g., any age) or animal) by causing an audio stimulus signal to be emitted from the transducer 114 coupled to the ABR test system 120 via a conductor 115. The ABR test system 120 may monitor an electrode 110 and an electrode 112 coupled to the ABR test system 120 via conductor 111 and conductor 113, respectively, to capture neural activity as ABR waveform samples that provide an indication as to how the auditory neural pathway responds to sound as it travels from the subject’s ear through a network of auditory nerves to the brainstem. Characteristics (e.g., latency, waveform shape and size, amplitude, etc.) of the ABR waveform samples captured during an ABR test may be evaluated to determine the health of the subject’s auditory system. In some examples, the electrode 110 and the electrode 112 may be coupled to the ABR test system 120 wirelessly. The ABR test system 120 may process the ABR waveform samples received via the electrode 110 and the electrode 112 to determine an ABR threshold intensity. The ABR threshold intensity may indicate a minimum sound intensity hearing level for the subject 102. It is appreciated that the electrode 110 and / or the electrode 112 may be positioned in different locations on the head of the subject without departing from the scope of the disclosure. It is also appreciated that more than two electrodes may be used during the ABR test without departing from the scope of the disclosure.

[0019] The ABR test system 120 may include an ABR test device 122, an ABR signal processing device 124, and an ABR diagnostic device 126. In some examples, the ABR test device 122, the ABR signal processing device 124, and / or the ABR diagnostic device 126 may be housed in a single device / casing and may share hardware, including memory and processor components. In other examples, the ABR test device 122, the ABR signal processing device 124, and / or the ABR diagnostic device 126 may be separate devices that are coupled together via a network 130 and a network 132. The network 130 and the network 132 may include any wired or wireless communications protocol, including wide area networks, local area networks, Wi-Fi, Bluetooth or other shortwave communication protocol, or any combination thereof.

[0020] The ABR test device 122 may cause the audio stimulus signal (e.g., any tone, frequency, or other short-duration audio stimulus (e.g., click or a chirp) at a particular frequency or series of frequencies within a normal hearing typical of a subject under test (e.g., such as frequencies between 20-20,000 Hz for humans (e.g., ABR test may use frequencies between 250 and 16,000 Hz for humans) or between 4,000 and 45,200 Hz for mice) to be emitted through the transducer 114 at different intensities (e.g., amplitudes or sound levels), and may monitor the electrode 110 and the electrode 112 placed on the subject 102 to capture the neural activity as ABR waveform samples having indicia of a response of the subject’s auditory neural pathway to sound as it travels from the subject’s ear through a network of auditory nerves to the brainstem. During an ABR test, the audio stimulus signal may be repeated many times for each sound intensity to gather many corresponding ABR waveform samples.

[0021] The ABR signal processing device 124 may analyze the ABR waveform samples to measure a response of the subject’s auditory neural pathway to sound as it travels from the subject’s ear through a network of auditory nerves to the brainstem responsive to the audio stimulus signals. In some examples, the ABR signal processing device 124 may analyze at least two sound intensities of the sound intensities to determine an ABR threshold intensity. For each of the at least two sound intensities, the analysis may include statistical bootstrapping, which may include several iterations of 1) randomly separating the ABR waveform samples into two groups, 2) calculating a median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution for the iterations for a given sound intensity may be analyzed to determine a mean. The mean crosscorrelation values for the at least two sound intensities may be fit to a sigmoid or powerlaw curve to determine the ABR threshold intensity. In other examples, the ABR signal processing device 124 may provide a distribution of estimated thresholds by performing multiple fits to the individual correlation coefficients, instead of or in addition to performing one sigmoid or power-law fit to the mean of the correlation coefficients.

[0022] Based on the ABR threshold intensity, the ABR diagnostic device 126 may generate a diagnosis or treatment plan for the subject 102. The diagnosis may include a diagnosis related to the auditory system of the subject 102, such as an issue with conductive hearing loss, sensorineural hearing loss, or a combination of both. A treatment plan may include medications, recommendation of a medical procedure to treat hearing loss, recommendation of one or more assistive hearing devices, adjustmentor recommendation of adjustment to one or more assistive hearing devices, or any combination thereof. For example, the recommendation(s) may be generally consistent with the guidelines described in “Year 2019 Position Statement: Principles and Guidelines for Early Hearing Detection and Intervention Programs” (2019) published by the National Center for Hearing Assessment and Management in The Journal of Early Hearing Detection and Intervention,(https: / / www. infanthearing. org / nhstc / docs / Year%202019%20JCIH%20Position%20St atement.pdf), which is incorporated by reference herein in its entirety.

[0023] In operation, the ABR test system 120 may be configured to conduct an ABR test on the subject 102. The subject 102 may include a human subject or a non-human subject. Typically, an ABR test may be performed on a subject that is incapable of providing reliable responsive feedback to a tester, such as an infant. In preparation for the test, the electrode 110 and the electrode 112 may be placed on the head of the subject 102 at particular locations to detect neural activity as ABR waveform samples that indicate a response of the subject’s auditory neural pathway to an audio stimulus signal emitted at different intensities as it travels from the subject’s ear through a network of auditory nerves to the brainstem and may be coupled to the ABR test system 120. The transducer 114 may be placed near or over one or both ears of the subject 102 and may be coupled to the ABR test system 120. The ABR test system 120 may perform the ABR test by causing the auditory stimulus signal to be emitted from the transducer 114. During the ABR test, the ABR test device 122 may cause the audio stimulus signal to be emitted from the transducer 114 at different intensities (e.g., amplitudes or sound levels). The range of intensities may include sound intensities determined based on the category of subject being tested. In some examples, the intensities used may be between and including 10 dB sound pressure level (SPL) to 120 dB SPL, with a more narrow range used without departing from the scope of the disclosure. The audio stimulus signal may be repeated many times for each sound intensity to gather many corresponding ABR waveform samples. The ABR test device 122 may monitor the electrode 110 and the electrode 112 to capture the ABR waveforms samples signals. The ABR test device 122 may store the ABR waveform samples in a database, in some examples.

[0024] The ABR signal processing device 124 may analyze the ABR waveform samples to measure a response of the subject’s auditory neural pathway to the audio stimulus signal emitted at different intensities as it travels from the subject’s earthrough a network of auditory nerves to the brainstem during the ABR test. The range ofintensities may include sound intensities determined based on the category of subject being tested. In some examples, the intensities used may be between and including 10 dB SPL to 120 dB SPL, with a more narrow range used without departing from the scope of the disclosure. In some examples, the analysis conducted by the ABR signal processing device 124 occurs in real-time as the ABR test is being conducted on the subject 102. In other examples, the analysis performed by the ABR signal processing device 124 occurs after the ABRtesting is completed. In some examples, the ABR signal processing device 124 may analyze the ABR waveform samples for at least two sound intensities of the sound intensities to determine an ABR threshold intensity. The ABR threshold intensity may indicate a minimum sound intensity hearing level for the subject 102. For each of the at least two sound intensities, the analysis may include statistical bootstrapping, which may include several iterations of 1) randomly separating the ABR waveform samples into two groups, 2) calculating a median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution for the many iterations for a given sound intensity may be analyzed to determine a mean. The mean cross-correlation values for the at least two sound intensities may be fit to a sigmoid or power-law curve to determine the ABR threshold intensity.

[0025] Based on the ABR threshold intensity, the ABR diagnostic device 126 may generate a diagnosis or treatment plan forthe subject 102. As noted above, the diagnosis may include a diagnosis related to the auditory system of the subject 102, such as an issue with conductive hearing loss, sensorineural hearing loss, or a combination of both. A treatment plan may include medications, recommendation of a medical procedure to treat hearing loss, recommendation of one or more assistive hearing devices, adjustment or recommendation of adjustment to one or more assistive hearing devices, or any combination thereof.

[0026] The statistical analysis process performed by the ABR test system 120 using statistical bootstrapping and the mean sound intensity of the cross-correlation distribution may provide more accurate and reliable results as compared with other ABR analysis techniques.

[0027] FIG. 2 is a schematic block diagram of an ABR test system 200, in accordance with embodiments described herein. The system 200 may include an ABR test device 222, a ABR signal processing device 224, a ABR diagnostic device 226, and a database 260. In some examples, the ABR test system 120 of FIG. 1 may implement elements ofthe system 200. The system 200 may perform an ABR test on a subject by causing an audio stimulus signal to be emitted from a transducer 214 coupled to the ABRtest device 222, and monitoring an electrode 210 and an electrode 212 coupled to the ABR test device 222 to capture neural activity as ABR waveform samples. In some examples, the electrode 210 and the electrode 212 may be coupled to the ABR test device 222 wirelessly. The ABR signal processing device 224 may process the ABR waveform samples received via the electrode 210 and the electrode 212 to determine an ABR threshold intensity. The ABR threshold intensity may indicate a minimum sound intensity hearing level for the subject.

[0028] In some examples, the ABRtest device 222, the ABR signal processing device 224, and / or the ABR diagnostic device 226 may be housed in a single device and may share hardware, including memory and processor components. In other examples, the ABRtest device 222, the ABR signal processing device 224, and / or the ABR diagnostic device 226 may be separate devices that are coupled together via a network 230 and a network 232. The network 230 and the network 232 may include any wired or wireless communications protocol, including wide area networks, local area networks, Wi-Fi, Bluetooth or other shortwave communication protocol, or any combination thereof.

[0029] The ABR test device 222 may include a transducer controller 240 and a receiver 242. The transducer controller 240 may cause the audio stimulus signal (e.g., a tone or tones at a particular frequency or series of frequencies) to be emitted through the transducer 214 at different intensities (e.g., amplitudes or sound levels). The receiver 242 may monitor the electrode 210 and the electrode 212 placed on the subject to capture neural activity as ABR waveform samples having indicia a response of the subject’s auditory neural pathway to the audio stimulus signal emitted at different intensities as it travels from the subject’s ear through a network of auditory nerves to the brainstem. The range of intensities may include sound intensities determined based on the category of subject being tested. In some examples, the intensities used may be between and including 10 dB SPL to 120 dB SPL, with a narrower range used without departing from the scope of the disclosure. The ABR waveform samples may be stored in the database 260. The receiver 242 may be configured to receive signals from the electrode 210 and the electrode 212 via a wired or a wireless connection. During an ABR test, the audio stimulus signal may be repeated many times for each sound intensity to gather many corresponding ABR waveform samples.

[0030] The ABR signal processing device 224 may include a special-purpose computer having one or more processor units 250 and memory 252. The memory 252 may store executable instructions 254, which, when executed by the one or more processor units 250, cause the one or more processor units 250 to retrieve the ABR waveform samples from the database 260 and analyze the ABR waveform samples to determine a health of the subject’s auditory neural pathway. In some examples, the executable instructions 254 may cause the one or more processor units 250 to analyze at least two sound intensities of the sound intensities to determine an ABR threshold intensity. For each of the at least two sound intensities, the analysis may include statistical bootstrapping, which may include several iterations of 1) randomly separating the ABR waveform samples into two groups, 2) calculating a median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution for the many iterations for a given sound intensity may be analyzed to determine a mean. The mean cross-correlation values for the at least two sound intensities may be fit to a sigmoid or power-law curve to determine the ABR threshold intensity.

[0031] Based on the ABR threshold intensity, the ABR diagnostic device 226 may generate a diagnosis or treatment plan for the subject. The diagnosis may include a diagnosis related to the auditory system of the subject, such as an issue with conductive hearing loss, sensorineural hearing loss, or a combination of both. A treatment plan may include medications, recommendation of a medical procedure to treat hearing loss, recommendation of one or more assistive hearing devices, adjustment or recommendation of adjustment to one or more assistive hearing devices, or any combination thereof. Additionally or alternatively, the ABR diagnostic device 226 may generate a training data set to train a ML tool. The training data set may be used to train the ML tool to determine the ABR threshold intensity based on ABR waveforms samples from an ABR test.

[0032] In operation, the system 200 may be configured to conduct an ABR test on the subject. The transducer controller 240 of the ABR test device 222 may perform the ABR test by causing a tone or tones to be emitted from the transducer 214. During the ABR test, the transducer controller 240 may cause an audio stimulus signal to be emitted from the transducer 214 at different intensities (e.g., amplitudes or sound levels). The audio stimulus signal may be repeated many times for each sound intensity to gather many corresponding ABR waveform samples. The receiver 242 may monitor the electrode210 and the electrode 212 to capture neural activity as ABR waveform samples. The ABRtest device 222 may store the ABR waveform samples in the database 260, in some examples.

[0033] The executable instructions 254 may cause the one or more processor units 250 of the ABR signal processing device 224 to analyze the ABR waveform samples to measure a response of the subject’s auditory neural pathway to the audio stimulus signal emitted at different intensities as it travels from the subject’s ear through a network of auditory nerves to the brainstem during the ABR test. In some examples, the analysis conducted by the ABR signal processing device 224 occurs in real-time as the ABRtest is being conducted on the subject. In other examples, the analysis performed by the ABR signal processing device 224 occurs after the ABR testing is completed. In some examples, the executable instructions 254 may cause the one or more processor units 250 to analyze the ABR waveform samples for at least two sound intensities of the sound intensities to determine an ABR threshold intensity. The ABR threshold intensity may indicate a minimum sound intensity hearing level for the subject. For each of the at least two sound intensities, the analysis may include statistical bootstrapping, which may include several iterations of 1) randomly separating the ABR waveform samples into two groups, 2) calculating a median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution for the many iterations for a given sound intensity may be analyzed to determine a mean. The mean cross-correlation values for the at least two sound intensities may be fit to a sigmoid or power-law curve to determine the ABR threshold intensity. . In other examples, the executable instructions 254 may cause the one or more processor units 250 to provide a distribution of estimated thresholds by performing multiple fits to the individual correlation coefficients, instead of or in addition to performing one sigmoid or power-law fit to the mean of the correlation coefficients.

[0034] Based on the ABR threshold intensity, the ABR diagnostic device 226 may generate a diagnosis or treatment plan for the subject. As noted above, the diagnosis may include a diagnosis related to the auditory system of the subject, such as an issue with conductive hearing loss, sensorineural hearing loss, or a combination of both. A treatment plan may include medications, recommendation of a medical procedure to treat hearing loss, recommendation of one or more assistive hearing devices, adjustment or recommendation of adjustment to one or more assistive hearing devices, or any combination thereof. Additionally or alternatively, the ABR diagnostic device 226 maygenerate a training data set to train a ML tool. The training data set may be used to train the ML tool to determine the ABR threshold intensity based on ABR waveforms samples from an ABR test.

[0035] The statistical analysis process performed by the system 200 using statistical bootstrapping and the mean sound intensity of the cross-correlation distribution may provide more accurate and reliable results as compared with other ABR analysis techniques.

[0036] FIG. 3 is flowchart of an example method 300 to determine an ABR threshold from ABR waveform samples, in accordance with embodiments described herein. The method 300 may be performed by the ABR test system 120 of FIG. 1, and / or the system 200 of FIG. 2

[0037] The method 300 may include pre-processing ABR waveform samples, at 310. For example, the ABR waveform samples may be passed through a pair of bandpass filters to remove noise and other extraneous information (e.g., information that is outside target frequency band caused by other brain activity corresponding to body movement and other sensory functions). The bandpass filters may have a frequency band between a lower bound LB and an upper bound UB. In some examples, the LB may be 300 Hz and the UB may be 3,000 Hz.

[0038] The method 300 may further include performing statistical bootstrapping for each sound intensity, at 320. For example, for each iteration of the bootstrapping process, the method 300 may include 1) randomly separating the ABR waveform samples into two groups, 2) calculating a median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms to determine a cross-correlation coefficient, at 320. The total number of bootstrap iterations K may include any positive number greater than 1, such as 100 or more, 200 or more, 500 or more, 1000 or more, etc. Statistical bootstrapping can take on many forms. For example, statistical bootstrapping may include standard bootstrapping (e.g., where the original data set is resampled with replacement), stratified bootstrapping (e.g., where the original data set is divided into strata, with each stratum then being resampled individually and the results combined), smooth bootstrapping (e.g., where random noise is added to the resamples to make them smoother), cluster bootstrapping (e.g., where the data contains "clusters" - observations that are expected to be correlated, and instead of resampling individual data points, entire clusters are resampled), parametric bootstrapping (e.g., including fitting a model to the data, then sampling from the modelto generate a bootstrap sample), or any combination thereof. In some examples, the standard bootstrapping may be used in the step 320. Once complete, the method 300 may include computing a mean, standard deviation, and cross-correlation distribution of the cross-correlation coefficients.

[0039] The method 300 may further include comparing the mean, standard deviation, and cross-correlation distribution across all intensities, at 330. The method 300 may further include determining an ABR threshold intensity, at 340. For example, the method 300 may include determining that the ABR threshold intensity is - / + infinity, at 345 and 348, when the mean, standard deviation, and cross-correlation distribution across all intensities provides an indicator that is greater than a prescribed criterion or is less than 1.1 times the prescribed criterion. Otherwise, the method 300 may include fitting the mean, standard deviation, and cross-correlation distribution across all intensities are fit to both a sigmoid curve, a 342, and a power-law curve, a 344. The method 300 may further include comparing the root-mean-square (RMS) of the sigmoid and power-law curves, a 344, and may determine that the ABS response threshold intensity is where the sigmoid crosses the prescribed criterion, at 346 when the RMS of the sigmoid curve is less than the RMS of the power-law curve. Otherwise, the method 300 includes determining that the ABS response threshold intensity is where the powerlaw curve crosses the prescribed criterion, at 347.

[0040] The blocks included in the described example method 300 are for illustration purposes. In some embodiments, these blocks may be performed in a different order. In some other embodiments, various blocks may be eliminated. In still other embodiments, various blocks may be divided into additional blocks, supplemented with other blocks, or combined together into fewer blocks. Other variations of these specific blocks are contemplated, including changes in the order of the blocks, changes in the content of the blocks being split or combined into other blocks, etc.

[0041] FIGs. 4A and 4B illustrate example signal charts 400 for an ABR test analysis, in accordance with embodiments described herein. The example signal charts 400 may be related steps performed during the method 300 of FIG. 3. The example signal plots 400 are exemplary and it is understood that results from other ABR tests may vary without departing from the scope of the disclosure.

[0042] The signal charts 410 include, for each intensity level from 20 dB SPL to 55 dB SPL, a plot of the sub-averages from one bootstrap iteration each of the two randomly generated groups (e.g., from step 322 of FIG. 3) (blue and orange), an ABRwaveform mean (black), and a standard error (gray). The charts 420 include a distribution of cross-correlation across all bootstraps for each intensity level from 20 dB SPL to 55 dB SPL, where lag is a time one average delayed against the other. The histogram charts 430 include a distribution of the lag at which the cross-correlation peaks from the cross-correlations of the signal charts 420 for each bootstrap iteration (e.g., from step 320 of FIG. 3) for each intensity level from 20 dB SPL to 55 dB SPL. The histogram charts 440 include a distribution of cross-correlation at a lag of zero (0) seconds across bootstraps (e.g., from step 320 of FIG. 3) for each intensity level from 20 dB SPL to 55 dB SPL. Using the histogram charts 440, the charts 450 provide fit of the data to both sigmoid and power-law curves using Kolmogorov-Smirnov Test of the zero lag distribution data (top chart), mean of the zero lag distribution data (middle chart), and peak of the zero lag distribution data (bottom chart). The point at which one of the sigmoid or power-law curves cross a selected criterion (e.g., as determined in step 340 of FIG. 3) may indicate the ABR threshold intensity.

[0043] FIG. 5 is flowchart of a method 500 to determine an ABR threshold from ABR waveform samples, in accordance with embodiments described herein. The method 500 may be performed by the ABR test system 120 of FIG. 1, and / or the system 200 of FIG.2.

[0044] In some examples, the method 500 may include during the ABR test, causing (e.g., via the ABR test device 122 of FIG. 1 and / or the transducer controller 240 of FIG. 2) an audio stimulus signal having an intensity of the plurality of intensities to be emitted via a sound transducer (e.g., the transducer 114 of FIG. l and / or the transducer 214 of FIG. 2). In some examples, the method 500 may further include during the ABR test, receiving (e.g., at the ABR test device 122 of FIG. 1 and / or the receiver 242 of FIG. 2), via a pair of electrodes (e.g., the electrode 110 and the electrode 112 of FIG. 1 and / or the electrode 210 and the electrode 212 of FIG. 2), the individual plurality of ABR waveform samples corresponding to an intensity of the plurality of intensities.

[0045] The method 500 may include receiving, at a computing device, for at least two sound intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples, at 510. The computing device may include the ABR signal processing device 124 of FIG. 1 and / or the ABR signal processing device 224 of FIG. 2. The individual plurality of ABR waveform samples may be received from a database, such as the database 260 of FIG. 2, in some examples.

[0046] The method 500 may further include generating, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of cross-correlation samples, at 520. The individual cross-correlation distributions may be generated by randomly selecting a first group of the individual plurality of ABR waveform samples to be allocated to a first subset, at 521; allocating a remaining group of the individual plurality of ABR waveform samples to a second subset, at 522; calculating, based on the first subset, a first median ABR waveform, at 523; calculating, based on the second subset, a second median ABR waveform, at 524; and calculating a cross-correlation between the first median ABR waveform and the second ABR median waveform to generate a cross-correlation sample of the plurality of cross-correlation samples, at 525.

[0047] The method 500 may further include calculating, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution, at 530.

[0048] The method 500 may further include identifying an ABR threshold intensity of the plurality of intensities by fitting the individual mean for at least two intensities of the plurality of intensities to a curve, at 540. In some examples, the method 500 may further include identifying the ABR threshold intensity by fitting the individual mean for the at least two intensities of the plurality of intensities to a sigmoid curve, and identifying where the threshold criterion is crossed on the sigmoid curve. In some examples, the method 500 may further include identifying the ABR threshold intensity by fitting the individual mean for the at least two intensities of the plurality of intensities to a power-law curve, and identifying where the threshold criterion is crossed on the power-law curve.

[0049] In some examples, the method 500 may further include generating (e.g., by the ABR diagnostic device 126 of FIG. 1 and / or the ABR diagnostic device 226 of FIG. 2) a diagnosis based on the ABR threshold intensity. In some examples, the method 500 may further include generating (e.g., by the ABR diagnostic device 126 of FIG. 1 and / or the ABR diagnostic device 226 of FIG. 2) a treatment plan based on the ABR threshold intensity. In some examples, the method 500 may further include configuring (e.g., by the ABR diagnostic device 126 of FIG. 1 and / or the ABR diagnostic device 226 of FIG. 2) an assistive hearing device based on the ABR threshold intensity.

[0050] The blocks included in the described example method 500 is for illustration purposes. In some embodiments, these blocks may be performed in a different order. Insome other embodiments, various blocks may be eliminated. In still other embodiments, various blocks may be divided into additional blocks, supplemented with other blocks, or combined together into fewer blocks. Other variations of these specific blocks are contemplated, including changes in the order of the blocks, changes in the content of the blocks being split or combined into other blocks, etc.

[0051] FIG. 6 is flowchart of a method 600 to determine an ABR threshold from ABR waveform samples, in accordance with embodiments described herein. The method 600 may be performed by the ABR test system 120 of FIG. 1, and / or the system 200 of FIG.2.

[0052] . In some examples, the method 600 may include during the ABR test, causing (e.g., via the ABR test device 122 of FIG. 1 and / or the transducer controller 240 of FIG. 2) an audio stimulus signal having an intensity of the plurality of intensities to be emitted via a sound transducer (e.g., the transducer 114 of FIG. l and / or the transducer 214 of FIG. 2). In some examples, the method 600 may further include during the ABR test, receiving (e.g., at the ABR test device 122 of FIG. 1 and / or the receiver 242 of FIG. 2), via a pair of electrodes (e.g., the electrode 110 and the electrode 112 of FIG. 1 and / or the electrode 210 and the electrode 212 of FIG. 2), the individual plurality of ABR waveform samples corresponding to an intensity of the plurality of intensities.

[0053] The method 600 may include receiving, at a computing device, for at least two intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples, at 610. The computing device may include the ABR signal processing device 124 of FIG. 1 and / or the ABR signal processing device 224 of FIG. 2. The individual plurality of ABR waveform samples may be received from a database, such as the database 260 of FIG. 2, in some examples.

[0054] The method 600 may further include generating, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of cross-correlation samples generated by iterative bootstrap sample signal processing of the individual plurality of ABR waveform samples, at subject 620. In some examples, the method 600 may further include calculating a cross-correlation sample of the plurality of cross-correlation samples by measuring a cross-correlation between a first group of randomly selected samples of the individual plurality of ABR waveform samples and a remaining group of the individual plurality of ABR waveform samples. In some examples, the method 600 may further include calculating the cross-correlation sample of the plurality of cross-correlation samples by measuring a crosscorrelation between a first median ABR waveform generated from the first group of randomly selected samples of the individual plurality of ABR waveform samples and a second median ABR waveform generated from the remaining group of the individual plurality of ABR waveform samples.

[0055] The method 600 may further include calculating, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution, at 630.

[0056] The method 600 may further include identifying an ABR threshold intensity of the plurality of intensities by comparing the individual mean for the at least two intensities of the plurality of intensities to a threshold criterion, at 640. In some examples, the method 600 may further include identifying the ABR threshold intensity by fitting the individual mean for the at least two intensities of the plurality of intensities to a sigmoid curve, and identifying where the threshold criterion is crossed on the sigmoid curve. In some examples, the method 600 may further include identifying the ABR threshold intensity by fitting the individual mean for the at least two intensities of the plurality of intensities to a power-law curve, and identifying where the threshold criterion is crossed on the power-law curve.

[0057] In some examples, the method 600 may further include generating (e.g., by the ABR diagnostic device 126 of FIG. 1 and / or the ABR diagnostic device 226 of FIG. 2) a diagnosis based on the ABR threshold intensity. In some examples, the method 600 may further include generating (e.g., by the ABR diagnostic device 126 of FIG. 1 and / or the ABR diagnostic device 226 of FIG. 2) a treatment plan based on the ABR threshold intensity. In some examples, the method 600 may further include configuring (e.g., by the ABR diagnostic device 126 of FIG. 1 and / or the ABR diagnostic device 226 of FIG. 2) an assistive hearing device based on the ABR threshold intensity.

[0058] The blocks included in the described example method 600 is for illustration purposes. In some embodiments, these blocks may be performed in a different order. In some other embodiments, various blocks may be eliminated. In still other embodiments, various blocks may be divided into additional blocks, supplemented with other blocks, or combined together into fewer blocks. Other variations of these specific blocks are contemplated, including changes in the order of the blocks, changes in the content of the blocks being split or combined into other blocks, etc.

[0059] EXAMPLES:

[0060] The following examples described in FIGs. 7-9 are illustrative and are not intended to limit the scope of the application.

[0061] FIG. 7 illustrates example charts 700 of a comparison between ABR threshold intensity determination using the methods described in FIGs. 1-6 (e.g., “XCsubB” method) compared to the Suthakar-Liberman (See Hear Res. 2019 Sep 15:381 : 107782. doi: 10.1016 / j.heares.2019.107782. Epub 2019 Aug 8. PMID: 31437652; PMCID: PMC6726521) method using a common data set. For a common data set tested, the heat map 710 indicates differences between human classification and the XCsubB method and the heat map 720 indicates differences between human classification and the Suthakar-Liberman method. The table 730 indicates accuracy (e.g., within + / - 7.5 dB and + / - 12.5 dB of human classification) of the XCsubB method and the Suthakar- Liberman method for the given data set, as well as failure percentage of each method. As shown, the XCsubB method was more much more accurate, and had a much lower failure percentage than the Suthakar-Liberman method.

[0062] FIG. 8 illustrates example charts 800 of a comparison between human classification to ABR threshold intensity determination using the methods described in FIGs. 1-6 (e.g., “XCsubB” method) using a common data set of 1950 thresholds across 3 strains, 12 genotypes, 8 frequencies (e.g., 4,000-45,200 Hz). For a common data set tested, the scatter plot 810 indicates differences between human classification and the XCsubB method and the heat map 820 indicates differences between human classification and the XCsubB method. As shown, the XCsubB method strongly correlates to the human classification.

[0063] FIG. 9 illustrates example charts 900 of a comparison between human classification to ABR threshold intensity determination using the methods described in FIGs. 1-6 (e.g., “XCsubB” method) using a common acoustic crossover data set. For a common data set tested, the charts 910 indicate human classification and the charts 920 indicate XCsubB classification. As shown, the XCsubB method strongly matches the human classification.

[0064] While the methods and systems have been described in connection with preferred embodiments and specific examples, it is not intended that the scope be limited to the particular embodiments set forth, as the embodiments herein are intended in all respects to be illustrative rather than restrictive.

[0065] Unless otherwise expressly stated, it is in no way intended that any method set forth herein be construed as requiring that its steps be performed in a specific order. Accordingly, where a method claim does not actually recite an order to be followed by its steps or it is not otherwise specifically stated in the claims or descriptions that the steps are to be limited to a specific order, it is in no way intended that an order be inferred, in any respect. This holds for any possible non-express basis for interpretation, including: matters of logic with respect to arrangement of steps or operational flow; plain meaning derived from grammatical organization or punctuation; the number or type of embodiments described in the specification.

[0066] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the method and compositions described herein. Such equivalents are intended to be encompassed by the following claims.

Claims

CLAIMSWhat is claimed is:

1. A method, comprising: receiving, at a computing device, for at least two sound intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples; generating, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of crosscorrelation samples by: randomly selecting a first group of the individual plurality of ABR waveform samples to be allocated to a first subset; allocating a remaining group of the individual plurality of ABR waveform samples to a second subset; calculating, based on the first subset, a first median ABR waveform; calculating, based on the second subset, a second median ABR waveform; and calculating a cross-correlation between the first median ABR waveform and the second ABR median waveform to generate a cross-correlation sample of the plurality of cross-correlation samples; and calculating, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution; and identifying an ABR threshold intensity of the plurality of intensities by comparing the individual mean for at least two intensities of the plurality of intensities to a threshold criterion.

2. The method of claim 1, further comprising generating a diagnosis based on the ABR threshold intensity.

3. The method of claim 1 , further comprising generating a treatment plan based on the ABR threshold intensity.

4. The method of claim 1, further comprising identifying the ABR threshold intensity by: fitting the individual mean for the at least two intensities of the plurality of intensities to a sigmoid curve; and identifying where the threshold criterion is crossed on the sigmoid curve.

5. The method of claim 1, further comprising identifying the ABR threshold intensity by: fitting the individual mean for the at least two intensities of the plurality of intensities to a power-law curve; and identifying where the threshold criterion is crossed on the power-law curve.

6. The method of claim 1, further comprising configuring an assistive hearing device based on the ABR threshold intensity.

7. The method of claim 1, further comprising, during the ABR test, causing an audio stimulus signal having an intensity of the plurality of intensities to be emitted via a sound transducer.

8. The method of claim 1, further comprising, during the ABR test, receiving, via a pair of electrodes, the individual plurality of ABR waveform samples corresponding to an intensity of the plurality of intensities.

9. A method, comprising: receiving, at a computing device, for at least two intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples; generating, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of cross-correlation samples generated by iterative bootstrap sample signal processing of the individual plurality of ABR waveform samples;calculating, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution; and identifying an ABR threshold intensity of the plurality of intensities by comparing the individual mean for the at least two intensities of the plurality of intensities to a threshold criterion.

10. The method of claim 9, further comprising calculating a cross-correlation sample of the plurality of cross-correlation samples by measuring a cross-correlation between a first group of randomly selected samples of the individual plurality of ABR waveform samples and a remaining group of the individual plurality of ABR waveform samples.

11. The method of claim 10, further comprising calculating the cross-correlation sample of the plurality of cross-correlation samples by measuring a cross-correlation between a first median ABR waveform generated from the first group of randomly selected samples of the individual plurality of ABR waveform samples and a second median ABR waveform generated from the remaining group of the individual plurality of ABR waveform samples.

12. The method of claim 9, further comprising generating a diagnosis based on the ABR threshold intensity.

13. The method of claim 9, further comprising generating a treatment plan based on the ABR threshold intensity.

14. The method of claim 9, further comprising identifying the ABR threshold intensity by: fitting the individual mean for the at least two intensities of the plurality of intensities to a sigmoid curve; and identifying where the threshold criterion is crossed on the sigmoid curve.

15. The method of claim 9, further comprising identifying the ABR threshold intensity by: fitting the individual mean for the at least two intensities of the plurality of intensities to a power-law curve; andidentifying where the threshold criterion is crossed on the power-law curve.

16. The method of claim 9, further comprising configuring an assistive hearing device based on the ABR threshold intensity.

17. The method of claim 9, further comprising, during the ABR test, causing an audio stimulus signal having an intensity of the plurality of intensities to be emitted via a sound transducer.

18. The method of claim 9, further comprising, during the ABR test, receiving, via a pair of electrodes, the individual plurality of ABR waveform samples corresponding to an intensity of the plurality of intensities.

19. A non-transitory computer readable medium storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: receive for at least two sound intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples; generate, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of cross-correlation samples by: randomly select a first group of the individual plurality of ABR waveform samples to be allocated to a first subset; allocate a remaining group of the individual plurality of ABR waveform samples to a second subset; calculate, based on the first subset, a first median ABR waveform; calculate, based on the second subset, a second median ABR waveform; and calculate a cross-correlation between the first median ABR waveform and the second ABR median waveform to generate a cross-correlation sample of the plurality of cross-correlation samples; and calculate, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution; andidentify an ABR threshold intensity of the plurality of intensities by comparing the individual mean for at least two intensities of the plurality of intensities to a threshold criterion.

20. The non-transitory computer readable medium of claim 19, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to generate a diagnosis based on the ABR threshold intensity.

21. The non-transitory computer readable medium of claim 19, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to generate a treatment plan based on the ABR threshold intensity.

22. The non-transitory computer readable medium of claim 19, wherein the processor-executable instructions, when executed by the at least one processor, that cause the at least one processor to identify the ABR threshold intensity further cause the at least one processor to: fit the individual mean for the at least two intensities of the plurality of intensities to a sigmoid curve; and identify where the threshold criterion is crossed on the sigmoid curve.

23. The non-transitory computer readable medium of claim 19, wherein the processor-executable instructions, when executed by the at least one processor, that cause the at least one processor to identify the ABR threshold intensity further cause the at least one processor to: fit the individual mean for the at least two intensities of the plurality of intensities to a power-law curve; and identify where the threshold criterion is crossed on the power-law curve.

24. The non-transitory computer readable medium of claim 19, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to configure an assistive hearing device based on the ABR threshold intensity.

25. The non-transitory computer readable medium of claim 19, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to, during the ABRtest, cause an audio stimulus signal having an intensity of the plurality of intensities to be emitted via a sound transducer.

26. The non-transitory computer readable medium of claim 19, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to, during the ABRtest, receive, via a pair of electrodes, the individual plurality of ABR waveform samples corresponding to an intensity of the plurality of intensities.

27. A non-transitory computer readable medium storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: receive for at least two intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples; generate, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of cross-correlation samples generated by iterative bootstrap sample signal processing of the individual plurality of ABR waveform samples; calculate, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution; and identify an ABR threshold intensity of the plurality of intensities by comparing the individual mean for the at least two intensities of the plurality of intensities to a threshold criterion.

28. The non-transitory computer readable medium of claim 27, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to calculate a cross-correlation sample of the plurality of crosscorrelation samples by measuring a cross-correlation between a first group of randomly selected samples of the individual plurality of ABR waveform samples and a remaining group of the individual plurality of ABR waveform samples.

29. The non-transitory computer readable medium of claim 28, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to calculate the cross-correlation sample of the plurality of crosscorrelation samples by measuring a cross-correlation between a first median ABR waveform generated from the first group of randomly selected samples of the individual plurality of ABR waveform samples and a second median ABR waveform generated from the remaining group of the individual plurality of ABR waveform samples.

30. The non-transitory computer readable medium of claim 27, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to generate a diagnosis based on the ABR threshold intensity.

31. The non-transitory computer readable medium of claim 27, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to generate a treatment plan based on the ABR threshold intensity.

32. The non-transitory computer readable medium of claim 27, wherein the processor-executable instructions, when executed by the at least one processor, that cause the at least one processor to identify the ABR threshold intensity further cause the at least one processor to: fit the individual mean for the at least two intensities of the plurality of intensities to a sigmoid curve; and identify where the threshold criterion is crossed on the sigmoid curve.

33. The non-transitory computer readable medium of claim 27, wherein the processor-executable instructions, when executed by the at least one processor, that cause the at least one processor to identify the ABR threshold intensity further cause the at least one processor to: fit the individual mean for the at least two intensities of the plurality of intensities to a power-law curve; and identify where the threshold criterion is crossed on the power-law curve.

34. The non-transitory computer readable medium of claim 27, wherein the processor-executable instructions, when executed by the at least one processor, further causethe at least one processor to configure an assistive hearing device based on the ABR threshold intensity.

35. The non-transitory computer readable medium of claim 27, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to, during the ABR test, cause an audio stimulus signal having an intensity of the plurality of intensities to be emitted via a sound transducer.

36. The non-transitory computer readable medium of claim 27, wherein the processor-executable instructions, when executed by the at least one processor, further cause the at least one processor to, during the ABR test, receive, via a pair of electrodes, the individual plurality of ABR waveform samples corresponding to an intensity of the plurality of intensities.

37. A system, comprising: a first computing device configured to: receive for at least two sound intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples; generate, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of crosscorrelation samples by: randomly select a first group of the individual plurality of ABR waveform samples to be allocated to a first subset; allocate a remaining group of the individual plurality of ABR waveform samples to a second subset; calculate, based on the first subset, a first median ABR waveform; calculate, based on the second subset, a second median ABR waveform; and calculate a cross-correlation between the first median ABR waveform and the second ABR median waveform to generate a cross-correlation sample of the plurality of cross-correlation samples; and calculate, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution; andidentify an ABR threshold intensity of the plurality of intensities by comparing the individual mean for at least two intensities of the plurality of intensities to a threshold criterion; and a second computing device configured to receive the identified ABR threshold intensity.

38. The system of claim 37, wherein the first computing device is further configured to generate a diagnosis based on the ABR threshold intensity.

39. The system of claim 37, wherein the first computing device is further configured to generate a treatment plan based on the ABR threshold intensity.

40. The system of claim 37, wherein the first computing device configured to identify the ABR threshold intensity includes the first computing device further configured to: fit the individual mean for the at least two intensities of the plurality of intensities to a sigmoid curve; and identify where the threshold criterion is crossed on the sigmoid curve.

41. The system of claim 37, wherein the first computing device configured to identify the ABR threshold intensity includes the first computing device further configured to: fit the individual mean for the at least two intensities of the plurality of intensities to a power-law curve; and identify where the threshold criterion is crossed on the power-law curve.

42. The system of claim 37, wherein the first computing device is further configured to configure an assistive hearing device based on the ABR threshold intensity.

43. The system of claim 37, wherein the first computing device is further configured to, during the ABR test, cause an audio stimulus signal having an intensity of the plurality of intensities to be emitted via a sound transducer.

44. The system of claim 37, wherein the first computing device is further configured to, during the ABR test, receive, via a pair of electrodes, the individual plurality of ABR waveform samples corresponding to an intensity of the plurality of intensities.

45. A system , comprising: a first computing device configured to: receive for at least two intensities of a plurality of intensities emitted during an auditory brainstem response (ABR) test, an individual plurality of ABR waveform samples; generate, for the at least two intensities of the plurality of intensities, an individual cross-correlation distribution including a plurality of crosscorrelation samples generated by iterative bootstrap sample signal processing of the individual plurality of ABR waveform samples; calculate, for the at least two intensities of the plurality of intensities, based on the plurality of cross-correlation samples, an individual mean of the individual cross-correlation distribution; and identify an ABR threshold intensity of the plurality of intensities by comparing the individual mean for the at least two intensities of the plurality of intensities to a threshold criterion; and a second computing device configured to receive the identified ABR threshold intensity.

46. The system of claim 45, wherein the first computing device is further configured to calculate a cross-correlation sample of the plurality of cross-correlation samples by measuring a cross-correlation between a first group of randomly selected samples of the individual plurality of ABR waveform samples and a remaining group of the individual plurality of ABR waveform samples.

47. The system of claim 46, wherein the first computing device is further configured to calculate the cross-correlation sample of the plurality of cross-correlation samples by measuring a cross-correlation between a first median ABR waveform generated from the first group of randomly selected samples of the individual plurality of ABR waveform samples and a second median ABR waveform generated from the remaining group of the individual plurality of ABR waveform samples.

48. The system of claim 45, wherein the first computing device is further configured to generate a diagnosis based on the ABR threshold intensity.

49. The system of claim 45, wherein the first computing device is further configured to generate a treatment plan based on the ABR threshold intensity.

50. The system of claim 45, wherein the first computing device configured to identify the ABR threshold intensity includes the first computing device further configured to: fit the individual mean for the at least two intensities of the plurality of intensities to a sigmoid curve; and identify where the threshold criterion is crossed on the sigmoid curve.

51. The system of claim 45, wherein the first computing device configured to identify the ABR threshold intensity includes the first computing device further configured to: fit the individual mean for the at least two intensities of the plurality of intensities to a power-law curve; and identify where the threshold criterion is crossed on the power-law curve.

52. The system of claim 45, wherein the first computing device is further configured to configure an assistive hearing device based on the ABR threshold intensity.

53. The system of claim 45, wherein the first computing device is further configured to, during the ABR test, cause an audio stimulus signal having an intensity of the plurality of intensities to be emitted via a sound transducer.

54. The system of claim 45, wherein the first computing device is further configured to, during the ABR test, receive, via a pair of electrodes, the individual plurality of ABR waveform samples corresponding to an intensity of the plurality of intensities.

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