Method and system for auditory brainstem response analysis

Abstract

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

Description

[0001] Cross-referencing of related patent applications

[0002] This application claims priority to U.S. Provisional Application No. 63 / 549,024, filed February 2, 2024, which is incorporated herein by reference in its entirety. Background Technology

[0003] Hearing or auditory testing can be performed on humans and animals for a variety of reasons, including detecting hearing impairment / loss or hearing sensitivity in test subjects, testing the effectiveness of hearing loss interventions, and collecting baseline hearing performance data. Various types of hearing or auditory tests can be selected based on the type of analysis performed and the subject's ability to participate in the test. For example, standard pure-tone auditory testing can be performed on subjects capable of responding to verbal commands to detect and indicate the presence of a specific pitch to the tester. Other types of auditory tests that rely on physical observation of the subject can also be performed. However, for many types of auditory tests where subjects cannot actively participate by providing feedback, determining the results can be very time-consuming and somewhat subjective. Overall, a more robust model for determining auditory test results is needed. Summary of the Invention

[0004] This application describes a method for determining auditory thresholds using the results of an auditory brainstem response (ABR) test. An ABR test (also described as measuring auditory evoked potentials) can provide indications about how the inner ear (e.g., the cochlea) and auditory pathways in the brain function. An ABR test can be performed by emitting an audio stimulus signal (e.g., one or more tones at a specific frequency or a series of frequencies) at different intensities (e.g., amplitude or sound level) through a device containing a transducer (e.g., headphones or other speakers), and measuring auditory nerve response activity via electrodes placed on the subject's head to detect the response to the emitted tones. The electrodes can be coupled to a dedicated computer. During the ABR test, the audio stimulus signal can be repeated multiple times for each sound intensity to collect numerous corresponding ABR waveform samples.

[0005] ABR threshold strength can be determined by analyzing ABR waveform samples corresponding to at least two sound intensities. The ABR threshold strength indicates the minimum sound intensity hearing level of a tested subject. For each of the at least two sound intensities, the analysis may include statistical self-help, which may include several iterations of: 1) randomly dividing the ABR waveform samples into two groups, 2) calculating the median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution over multiple iterations for a given sound intensity can be analyzed to determine the mean. The average cross-correlation value of the at least two sound intensities can be fitted to an sigmoid or power-law curve to determine the ABR threshold strength. Compared to other ABR analysis techniques, the statistical analysis process using the average sound intensity with statistical self-help and cross-correlation distribution provides more accurate and reliable results.

[0006] Further advantages of the disclosed methods and compositions will be set forth in part in the description which follows, and will be understood in part from the description, or may be learned by practice of the disclosed methods and compositions. The advantages of the disclosed methods and compositions will be realized and obtained by means of the elements and combinations particularly pointed out in the appended claims. It should be understood that the foregoing general description and the following detailed description are merely exemplary and explanatory, and do not limit the claimed invention. Attached Figure Description

[0007] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate some embodiments of the disclosed methods and compositions and, together with the specification, serve to explain the principles of the disclosed methods and compositions.

[0008] Figure 1 This is a schematic block diagram of an ABR test system according to an embodiment described herein.

[0009] Figure 2 This is a schematic block diagram of an ABR test system according to an embodiment described herein.

[0010] Figure 3 This is a flowchart of a method for determining an ABR threshold based on ABR waveform samples according to an embodiment described herein.

[0011] Figure 4A and Figure 4B An example signal diagram illustrating ABR test analysis according to the embodiments described herein is shown.

[0012] Figure 5 This is a flowchart of a method for determining an ABR threshold based on ABR waveform samples according to an embodiment described herein.

[0013] Figure 6 This is a flowchart of a method for determining an ABR threshold based on ABR waveform samples according to an embodiment described herein.

[0014] Figures 7 to 9 This includes various exemplary illustrations using experimental data from the ABR threshold intensity classification method described herein. Detailed Implementation

[0015] The disclosed systems, devices, apparatuses, and methods can be more readily understood by referring to the following detailed description of specific embodiments and the examples and diagrams contained therein, as well as their preceding and subsequent descriptions.

[0016] It should be understood that the disclosed systems, devices, apparatuses, and methods are not limited to the specific methods, schemes, and reagents described, as these can vary. It should also be understood that the terminology used herein is merely for describing particular embodiments and is not intended to limit the scope of the invention, which is defined only by the appended claims.

[0017] It must be noted that, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the,” as used herein and in the appended claims, include plural references. Thus, for example, reference to “antibody” includes a plurality of such antibodies, reference to “antibody” refers to one or more antibodies and their equivalents known to those skilled in the art, and so on.

[0018] Throughout the specific embodiments and claims of this specification, the word "comprise" and variations thereof, such as "comprising" and "comprises," mean "including but not limited to" and are not intended to exclude, for example, other additives, components, integers, or steps. In particular, in methods stated to include one or more steps or operations, each step is specifically considered to include the listed contents (unless the step includes restrictive terms such as "consisting of"), meaning that each step is not intended to exclude, for example, other additives, components, integers, or steps not listed in the step description.

[0019] Figure 1This is a schematic block diagram of an ABR testing system 100 according to embodiments described herein. System 100 includes an ABR testing system 120 coupled to electrode 110 via conductor 111, electrode 112 via conductor 113, and transducer 114 via conductor 115. ABR testing system 120 can perform an ABR test on a subject 102 (e.g., a human (e.g., of any age) by transmitting an audio stimulus signal from transducer 114 coupled to ABR testing system 120 via conductor 115. ABR testing system 120 can monitor electrodes 110 and 112 coupled to ABR testing system 120 via conductors 111 and 113, respectively, to capture neural activity as ABR waveform samples, providing an indication of how the auditory neural pathway responds to sound as it travels from the subject's ear through the auditory neural network to the brainstem. Characteristics of the ABR waveform samples captured during ABR testing (e.g., delay, waveform shape and size, amplitude, etc.) can be evaluated to determine the health status of the subject's auditory system. In some examples, electrodes 110 and 112 may be wirelessly coupled to ABR testing system 120. ABR testing system 120 may process ABR waveform samples received via electrodes 110 and 112 to determine ABR threshold intensity. ABR threshold intensity may indicate the minimum sound intensity hearing level of subject 102. It should be understood that electrodes 110 and / or 112 may be positioned at different locations on the subject's head without departing from the scope of this disclosure. It should also be understood that more than two electrodes may be used during ABR testing without departing from the scope of this disclosure.

[0020] ABR test system 120 may include ABR test device 122, ABR signal processing device 124, and ABR diagnostic device 126. In some examples, ABR test device 122, ABR signal processing device 124, and / or ABR diagnostic device 126 may be housed in a single device / enclosure and may share hardware, including memory and processor components. In other examples, ABR test device 122, ABR signal processing device 124, and / or ABR diagnostic device 126 may be separate devices coupled together via networks 130 and 132. Networks 130 and 132 may include any wired or wireless communication protocols, including wide area networks, local area networks, Wi-Fi, Bluetooth, or other shortwave communication protocols, or any combination thereof.

[0021] The ABR testing device 122 can transmit audio stimulus signals (e.g., any tone, frequency, or other short-duration audio stimulus (e.g., a click or chirp)) at specific frequencies or a range of frequencies within the typical normal hearing range of the tested subject (e.g., frequencies between 20 Hz and 20,000 Hz for humans (e.g., frequencies between 250 Hz and 16,000 Hz for ABR testing in humans), or frequencies between 4,000 Hz and 45,200 Hz for mice) through transducer 114 at different intensities (e.g., amplitude or sound level). Electrodes 110 and 112 placed on the subject 102 can be monitored to capture neural activity as ABR waveform samples, which are labeled with the subject's auditory neural pathway response to sound as it travels from the subject's ear through the auditory neural network to the brainstem. During the ABR test, the audio stimulus signal can be repeated multiple times for each sound intensity to collect a large number of corresponding ABR waveform samples.

[0022] ABR signal processing device 124 can analyze ABR waveform samples to measure the auditory neural pathway's response to sound as it travels from a subject's ear through the auditory neural network to the brainstem in response to an audio stimulus signal. In some examples, ABR signal processing device 124 can analyze at least two sound intensities to determine the 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 dividing the ABR waveform samples into two groups, 2) calculating the median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution of the iterations for a given sound intensity can be analyzed to determine the mean. The average cross-correlation value of the at least two sound intensities can be fitted to an sigmoid or power-law curve to determine the ABR threshold intensity. In other examples, ABR signal processing device 124 may provide an estimated threshold distribution by performing multiple fits on the individual cross-correlation coefficients, rather than performing a single sigmoid or power-law fit on the mean of the cross-correlation coefficients, or in addition to performing a single sigmoid or power-law fit on the mean of the cross-correlation coefficients.

[0023] Based on the ABR threshold strength, the ABR diagnostic device 126 can generate a diagnosis or treatment plan for subject 102. The diagnosis may include diagnoses related to subject 102's auditory system, such as conductive hearing loss, sensorineural hearing loss, or a combination of both. The treatment plan may include recommendations for medications, medical procedures for treating hearing loss, recommendations for one or more assistive hearing devices, recommendations for adjustments or modifications to one or more assistive hearing devices, or any combination thereof. For example, recommendations may be substantially consistent with the guidelines described in the "2019 Position Statement: Principles and Guidelines for Early Hearing Detection and Intervention Programs" (2019) published by the National Center for Hearing Assessment and Administration in the journal Early Hearing Detection and Intervention (https: / / www.infanthearing.org / nhstc / docs / Year%202019%20JCIH%20Position%20Statement.pdf), which is incorporated herein by reference in its entirety.

[0024] In operation, the ABR testing system 120 can be configured to perform an ABR test on a subject 102. Subject 102 may be a human subject or a non-human subject. Typically, the ABR test can be performed on subjects who cannot provide reliable response feedback to the test subject, such as infants. To prepare for the test, electrodes 110 and 112 can be placed at specific locations on the head of the subject 102 to detect neural activity as ABR waveform samples. These ABR waveform samples indicate the response of the subject's auditory neural pathways to audio stimulus signals emitted at different intensities as the audio stimulus signal propagates from the subject's ears through the auditory neural network to the brainstem, and these electrodes can be coupled to the ABR testing system 120. A transducer 114 can be placed near or above one or both ears of the subject 102 and can be coupled to the ABR testing system 120. The ABR testing system 120 can perform the ABR test by emitting auditory stimulus signals from the transducer 114. During ABR testing, ABR testing device 122 can emit audio stimulus signals from transducer 114 at different intensities (e.g., amplitude or sound level). The intensity range can include sound intensities determined based on the category of the subject being tested. In some examples, the intensities used can be between 10 dB sound pressure level (SPL) and 120 dB SPL, and include both 10 dB SPL and 120 dB SPL; using a narrower range does not depart from the scope of this disclosure. For each sound intensity, the audio stimulus signal can be repeated multiple times to collect a number of corresponding ABR waveform samples. ABR testing device 122 can monitor electrodes 110 and 112 to capture ABR waveform sample signals. In some examples, ABR testing device 122 can store ABR waveform samples in a database.

[0025] The ABR signal processing device 124 can analyze ABR waveform samples to measure, during an ABR test, the response of a subject's auditory neural pathway to audio stimulus signals emitted at different intensities as the audio stimulus signal propagates from the subject's ear through the auditory neural network to the brainstem. The intensity range can include sound intensities determined based on the category of the subject being tested. In some examples, the intensities used can be between 10 dB SPL and 120 dB SPL, and include both 10 dB SPL and 120 dB SPL; using a narrower range does not depart from the scope of this disclosure. In some examples, the analysis performed by the ABR signal processing device 124 occurs in real time when the ABR test is performed on the subject 102. In other examples, the analysis performed by the ABR signal processing device 124 occurs after the ABR test is completed. In some examples, the ABR signal processing device 124 can analyze ABR waveform samples of at least two sound intensities to determine the ABR threshold intensity. The ABR threshold intensity can indicate the minimum sound intensity hearing level of the subject 102. For each of at least two sound intensities, the analysis may include statistical bootstrapping, which may include several iterations of: 1) randomly dividing the ABR waveform samples into two groups, 2) calculating the median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution over multiple iterations for a given sound intensity can be analyzed to determine the mean. The average cross-correlation value of at least two sound intensities can be fitted to an sigmoid or power-law curve to determine the ABR threshold intensity.

[0026] Based on the ABR threshold strength, the ABR diagnostic device 126 can generate a diagnosis or treatment plan for the subject 102. As described above, the diagnosis may include diagnoses related to the subject 102's auditory system, such as conductive hearing loss, sensorineural hearing loss, or a combination of both. The treatment plan may include recommendations for medications, medical procedures for treating hearing loss, recommendations for one or more assistive hearing devices, recommendations for adjustments to one or more assistive hearing devices, or any combination thereof.

[0027] Compared to other ABR analysis techniques, the statistical analysis process performed by the ABR testing system 120 using the average sound intensity of statistical bootstrap and cross-correlation distributions can provide more accurate and reliable results.

[0028] Figure 2 This is a schematic block diagram of an ABR testing system 200 according to embodiments described herein. System 200 may include ABR testing equipment 222, ABR signal processing equipment 224, ABR diagnostic equipment 226, and a database 260. In some examples, Figure 1The ABR testing system 120 can implement the elements of system 200. System 200 performs an ABR test on a subject by transmitting an audio stimulus signal from a transducer 214 coupled to ABR testing device 222 and monitoring electrodes 210 and 212 coupled to ABR testing device 222 to capture neural activity as ABR waveform samples. In some examples, electrodes 210 and 212 can be wirelessly coupled to ABR testing device 222. ABR signal processing device 224 can process the ABR waveform samples received via electrodes 210 and 212 to determine ABR threshold intensity. ABR threshold intensity can indicate the subject's minimum sound intensity hearing level.

[0029] In some examples, ABR test device 222, ABR signal processing device 224, and / or ABR diagnostic device 226 may be housed in a single device and may share hardware, including memory and processor components. In other examples, ABR test device 222, ABR signal processing device 224, and / or ABR diagnostic device 226 may be separate devices coupled together via networks 230 and 232. Networks 230 and 232 may include any wired or wireless communication protocols, including wide area networks, local area networks, Wi-Fi, Bluetooth, or other shortwave communication protocols, or any combination thereof.

[0030] ABR testing device 222 may include a transducer controller 240 and a receiver 242. The transducer controller 240 may transmit audio stimulus signals (e.g., one or more tones at a specific frequency or a series of frequencies) at different intensities (e.g., amplitude or sound level) through transducer 214. The receiver 242 may monitor electrodes 210 and 212 placed on a subject to capture neural activity as ABR waveform samples, which are labeled with the subject's auditory neural pathways' response to the audio stimulus signals transmitted at different intensities as the audio stimulus signals propagate from the subject's ear through the auditory neural network to the brainstem. The intensity range may include sound intensities determined based on the category of the subject being tested. In some examples, the intensities used may be between 10 dB SPL and 120 dB SPL, and the use of a narrower range does not depart from the scope of this disclosure. The ABR waveform samples may be stored in a database 260. The receiver 242 may be configured to receive signals from electrodes 210 and 212 via a wired or wireless connection. During the ABR test, the audio stimulus signal can be repeated multiple times for each sound intensity to collect many corresponding ABR waveform samples.

[0031] ABR signal processing device 224 may include a dedicated computer having one or more processor units 250 and a memory 252. The memory 252 may store executable instructions 254 that, when executed by the one or more processor units 250, cause the one or more processor units 250 to retrieve ABR waveform samples from a database 260 and analyze the ABR waveform samples to determine the health status of the subject's auditory neural pathways. In some examples, the executable instructions 254 may cause the one or more processor units 250 to analyze at least two sound intensities to determine the 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 dividing the ABR waveform samples into two groups, 2) calculating the median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution over multiple iterations of a given sound intensity may be analyzed to determine the mean. The average cross-correlation value of the at least two sound intensities may be fitted to an sigmoid curve or a power-law curve to determine the ABR threshold intensity.

[0032] Based on the ABR threshold intensity, the ABR diagnostic device 226 can generate a diagnosis or treatment plan for the subject. The diagnosis may include a diagnosis related to the subject's auditory system, such as conductive hearing loss, sensorineural hearing loss, or a combination of both. The treatment plan may include recommendations for medications, medical procedures for treating hearing loss, recommendations for one or more assistive hearing devices, recommendations for adjustments to one or more assistive hearing devices, or any combination thereof. Additionally or alternatively, the ABR diagnostic device 226 can generate a training dataset to train an ML tool. The training dataset can be used to train the ML tool to determine the ABR threshold intensity based on ABR waveform samples from ABR tests.

[0033] In operation, system 200 can be configured to perform an ABR test on a subject. The transducer controller 240 of the ABR testing device 222 can perform the ABR test by emitting one or more tones from transducer 214. During the ABR test, transducer controller 240 can emit audio stimulus signals from transducer 214 at different intensities (e.g., amplitude or sound level). For each sound intensity, the audio stimulus signal can be repeated multiple times to collect a number of corresponding ABR waveform samples. Receiver 242 can monitor electrodes 210 and 212 to capture neural activity as ABR waveform samples. In some examples, ABR testing device 222 can store the ABR waveform samples in database 260.

[0034] Executable instruction 254 may cause one or more processor units 250 of ABR signal processing device 224 to analyze ABR waveform samples to measure, during an ABR test, the response of a subject's auditory neural pathway to audio stimulus signals emitted at different intensities as the audio stimulus signal propagates from the subject's ear through the auditory neural network to the brainstem. In some examples, the analysis performed by ABR signal processing device 224 occurs in real time while the ABR test is performed on the subject. In other examples, the analysis performed by ABR signal processing device 224 occurs after the ABR test is completed. In some examples, executable instruction 254 may cause one or more processor units 250 to analyze ABR waveform samples of at least two sound intensities to determine an ABR threshold intensity. The ABR threshold intensity may indicate the subject's minimum sound intensity hearing level. For each of the at least two sound intensities, the analysis may include statistical bootstrapping, which may include several iterations of: 1) randomly dividing the ABR waveform samples into two groups, 2) calculating the median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms. The cross-correlation distribution over multiple iterations for a given sound intensity may be analyzed to determine the mean. The average cross-correlation values ​​of at least two sound intensities can be fitted to an S-shaped or power-law curve to determine the ABR threshold intensity. In other examples, executable instruction 254 may cause one or more processor units 250 to provide the distribution of the estimated threshold by performing multiple fits on the individual cross-correlation coefficients, rather than performing an S-shaped or power-law fit on the mean of the cross-correlation coefficients, or in addition to performing an S-shaped or power-law fit on the mean of the cross-correlation coefficients.

[0035] Based on the ABR threshold intensity, the ABR diagnostic device 226 can generate a diagnosis or treatment plan for the subject. As described above, the diagnosis may include diagnoses related to the subject's auditory system, such as conductive hearing loss, sensorineural hearing loss, or a combination of both. The treatment plan may include recommendations for medications, medical procedures for treating hearing loss, recommendations for one or more assistive hearing devices, recommendations for adjustments or modifications to one or more assistive hearing devices, or any combination thereof. Additionally or alternatively, the ABR diagnostic device 226 can generate a training dataset to train an ML tool. The training dataset can be used to train the ML tool to determine the ABR threshold intensity based on ABR waveform samples from ABR tests.

[0036] Compared to other ABR analysis techniques, the statistical analysis process performed by System 200 using the average sound intensity of statistical self-help and cross-correlation distributions can provide more accurate and reliable results.

[0037] Figure 3 This is a flowchart of an example method 300 for determining an ABR threshold based on ABR waveform samples, according to embodiments described herein. Method 300 can be... Figure 1 ABR test system 120 and / or Figure 2 The system 200 is running.

[0038] Method 300 may include preprocessing the ABR waveform sample at 310. For example, the ABR waveform sample may be passed through a pair of bandpass filters to remove noise and other irrelevant information (e.g., information outside the 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 the lower limit LB and the upper limit UB. In some examples, LB may be 300 Hz and UB may be 3,000 Hz.

[0039] Method 300 may further include, at 320, performing statistical bootstrapping for each sound intensity. For example, for each iteration of the bootstrapping process, method 300 may include, at 320, 1) randomly dividing the ABR waveform samples into two groups, 2) calculating the median waveform for each group, and 3) performing a normalized cross-correlation between the two median waveforms to determine the cross-correlation number. The total number K of bootstrapping iterations 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 various forms. For example, statistical bootstraps may include standard bootstraps (e.g., where the original dataset is resampled with replacement), hierarchical bootstraps (e.g., where the original dataset is divided into multiple strata, then each stratum is resampled individually and the results are combined), smoothing bootstraps (e.g., where random noise is added to the resampling to make it smoother), clustering bootstraps (e.g., where the data contains “clusters”—expected correlated observations—and the entire cluster is resampled instead of individual data points), parametric bootstraps (e.g., involving fitting a model to the data and then sampling from the model to generate bootstrap samples), or any combination thereof. In some examples, a standard bootstrap may be used in step 320. Once completed, method 300 may include calculating the mean, standard deviation, and cross-correlation distribution of the cross-correlation coefficients.

[0040] Method 300 may further include comparing the mean, standard deviation, and cross-correlation distributions of all intensities at 330. Method 300 may further include determining the ABR threshold intensity at 340. For example, Method 300 may include determining the ABR threshold intensity as - / + infinity at 345 and 348 when the mean, standard deviation, and cross-correlation distributions of all intensities provide an index greater than or less than 1.1 times the specified standard. In other cases, Method 300 may include fitting the mean, standard deviation, and cross-correlation distributions of all intensities to both an S-shaped curve 342 and a power-law curve 344. Method 300 may further include comparing the root mean square (RMS) of the S-shaped curve and the power-law curve at 344, and at 346, determining the position where the ABS response threshold intensity crosses the specified standard when the RMS of the S-shaped curve is less than the RMS of the power-law curve. In other cases, Method 300 includes determining at 347 that the ABS response threshold intensity is the position where the power-law curve crosses the specified standard.

[0041] The blocks included in the described example method 300 are for illustrative purposes. In some embodiments, these blocks may be executed in a different order. In some other embodiments, various blocks may be eliminated. In still other embodiments, individual blocks may be divided into additional blocks, supplemented with other blocks, or combined together to form fewer blocks. Other variations of these particular blocks are contemplated, including changes in the order of the blocks, changes in the content of blocks that are divided or combined into other blocks, etc.

[0042] Figure 4A and Figure 4B An example signal graph 400 for ABR test analysis is illustrated according to an embodiment described herein. The example signal graph 400 may be in... Figure 3 The relevant steps performed during method 300. Example signal diagram 400 is exemplary, and it should be understood that results from other ABR tests may be changed without departing from the scope of this disclosure.

[0043] Signal graph 410 includes a plot of the sub-means of one bootstrap iteration from each of two randomly generated groups for each intensity level from 20 dB SPL to 55 dB SPL (e.g., from...). Figure 3 Step 322 (blue and orange), ABR waveform mean (black), and standard error (grey). Figure 420 includes the cross-correlation distribution of all bootstraps for each intensity level from 20 dB SPL to 55 dB SPL, where lag is the time delay of one mean relative to another. Histogram Figure 430 includes the cross-correlation distribution of all bootstraps for each intensity level from 20 dB SPL to 55 dB SPL at each bootstrap iteration (e.g., from step 322) (blue and orange), ABR waveform mean (black), and standard error (grey). Figure 3The hysteresis distribution at the cross-correlation peak of the signal in step 320) is shown in graph 420. Histogram graph 440 includes cross-buffet data for each intensity level from 20 dB SPL to 55 dB SPL (e.g., from...). Figure 3 Step 320) shows the cross-correlation distribution at a lag of zero (0) seconds. Histograms 440 and 450 use the Kolmogorov-Smirnov test to provide a fit between the data and both an S-curve and a power-law curve. One of the S-curves or the power-law curve crosses the selected criteria at points (e.g., as shown in the diagram). Figure 3 The value determined in step 340 can indicate the ABR threshold strength.

[0044] Figure 5 This is a flowchart of a method 500 for determining an ABR threshold based on ABR waveform samples, according to embodiments described herein. Method 500 can be... Figure 1 ABR test system 120 and / or Figure 2 The system 200 is running.

[0045] In some examples, method 500 may be included during ABR testing (e.g., via...). Figure 1 ABR testing equipment 122 and / or Figure 2 The transducer controller 240) transmits an audio stimulus signal with multiple intensities via a sound transducer (e.g., Figure 1 Transducer 114 and / or Figure 2 Transducer 214) emits. In some examples, method 500 may further include during ABR testing (e.g., in Figure 1 ABR testing equipment 122 and / or Figure 2 The receiver at location 242) is located via a pair of electrodes (e.g., Figure 1 Electrode 110 and electrode 112 and / or Figure 2 Electrodes 210 and 212 receive multiple ABR waveform samples, each corresponding to an intensity among multiple intensities.

[0046] Method 500 may include, at 510, at a computing device, receiving respective plurality of ABR waveform samples for at least two of a plurality of sound intensities emitted during an auditory brainstem response (ABR) test. The computing device may include... Figure 1 ABR signal processing equipment 124 and / or Figure 2 The ABR signal processing device 224. In some examples, it can be obtained from a database (such as...) Figure 2 The database 260 receives multiple ABR waveform samples from each of its own.

[0047] Method 500 may further include, at 520, generating respective cross-correlation distributions comprising multiple cross-correlation samples for at least two of the multiple intensities. The respective cross-correlation distributions may be generated by: at 521, randomly selecting a first group of multiple ABR waveform samples to be assigned to a first subset; at 522, assigning the remaining groups of the multiple ABR waveform samples to a second subset; at 523, calculating a first median ABR waveform based on the first subset; at 524, calculating a second median ABR waveform based on the second subset; and at 525, calculating the cross-correlation between the first median ABR waveform and the second median ABR waveform to generate cross-correlation samples among the multiple cross-correlation samples.

[0048] Method 500 may further include, at 530, calculating the respective means of the respective cross-correlation distributions for at least two of the multiple intensities, based on multiple cross-correlation samples.

[0049] Method 500 may further include, at 540, identifying ABR threshold intensities of multiple intensities by fitting the respective means of at least two intensities of the multiple intensities to a curve. In some examples, method 500 may further include identifying ABR threshold intensities by fitting the respective means of at least two intensities of the multiple intensities to an sigmoid curve and identifying the crossing points of the threshold criterion on the sigmoid curve. In some examples, method 500 may further include identifying ABR threshold intensities by fitting the respective means of at least two intensities of the multiple intensities to a power-law curve and identifying the crossing points of the threshold criterion on the power-law curve.

[0050] In some examples, method 500 may further include (e.g., by...) Figure 1 ABR diagnostic equipment 126 and / or Figure 2 The ABR diagnostic device 226 generates a diagnosis based on the ABR threshold intensity. In some examples, method 500 may further include (e.g., by...) Figure 1 ABR diagnostic equipment 126 and / or Figure 2 The ABR diagnostic device 226 generates a treatment plan based on the ABR threshold intensity. In some examples, method 500 may further include (e.g., by...) Figure 1 ABR diagnostic equipment 126 and / or Figure 2 The ABR diagnostic device 226) is an assistive hearing device configured based on the ABR threshold intensity.

[0051] The blocks included in the described example method 500 are for illustrative purposes. In some embodiments, these blocks may be executed in a different order. In some other embodiments, various blocks may be eliminated. In still other embodiments, individual blocks may be divided into additional blocks, supplemented with other blocks, or combined together to form fewer blocks. Other variations of these particular blocks are contemplated, including changes in the order of the blocks, changes in the content of blocks that are divided or combined into other blocks, etc.

[0052] Figure 6 This is a flowchart of a method 600 for determining an ABR threshold based on ABR waveform samples, according to embodiments described herein. Method 600 can be... Figure 1 ABR test system 120 and / or Figure 2 The system 200 is running.

[0053] In some examples, method 600 may be included during ABR testing (e.g., via...). Figure 1 ABR testing equipment 122 and / or Figure 2 The transducer controller 240) transmits an audio stimulus signal with multiple intensities via a sound transducer (e.g., Figure 1 Transducer 114 and / or Figure 2 Transducer 214) emits. In some examples, method 600 may further include during ABR testing (e.g., in Figure 1 ABR testing equipment 122 and / or Figure 2 The receiver at location 242) is located via a pair of electrodes (e.g., Figure 1 Electrode 110 and electrode 112 and / or Figure 2 Electrodes 210 and 212 receive multiple ABR waveform samples, each corresponding to an intensity among multiple intensities.

[0054] Method 600 may include, at 610, at a computing device, receiving respective plurality of ABR waveform samples for at least two of a plurality of intensities emitted during an auditory brainstem response (ABR) test. The computing device may include... Figure 1 ABR signal processing equipment 124 and / or Figure 2 The ABR signal processing device 224. In some examples, it can be obtained from a database (such as...) Figure 2 The database 260 receives multiple ABR waveform samples from each of its own.

[0055] Method 600 may further include, at subject 620, generating respective cross-correlation distributions for at least two of a plurality of intensities, the respective cross-correlation distributions comprising a plurality of cross-correlation samples generated by iterative bootstrap sample signal processing of the respective plurality of ABR waveform samples. In some examples, method 600 may further include calculating cross-correlation samples among the plurality of cross-correlation samples by measuring the cross-correlation between a first set of randomly selected samples from the respective plurality of ABR waveform samples and the remaining sets of the respective plurality of ABR waveform samples. In some examples, method 600 may further include calculating cross-correlation samples among the plurality of cross-correlation samples by measuring the cross-correlation between a first median ABR waveform generated from the first set of randomly selected samples from the respective plurality of ABR waveform samples and a second median ABR waveform generated from the remaining sets of the respective plurality of ABR waveform samples.

[0056] Method 600 may further include, at 630, calculating the respective means of the respective cross-correlation distributions for at least two of the multiple intensities, based on multiple cross-correlation samples.

[0057] Method 600 may further include, at 640, identifying ABR threshold strengths of multiple intensities by comparing the respective means of at least two intensities of the multiple intensities with a threshold criterion. In some examples, method 600 may further include identifying ABR threshold strengths by fitting the respective means of at least two intensities of the multiple intensities to an sigmoid curve and identifying the crossing points of the threshold criterion on the sigmoid curve. In some examples, method 600 may further include identifying ABR threshold strengths by fitting the respective means of at least two intensities of the multiple intensities to a power-law curve and identifying the crossing points of the threshold criterion on the power-law curve.

[0058] In some examples, method 600 may further include (e.g., by...) Figure 1 ABR diagnostic equipment 126 and / or Figure 2 The ABR diagnostic device 226 generates a diagnosis based on the ABR threshold intensity. In some examples, method 600 may further include (e.g., by...) Figure 1 ABR diagnostic equipment 126 and / or Figure 2 The ABR diagnostic device 226 generates a treatment plan based on the ABR threshold intensity. In some examples, method 600 may further include (e.g., by...) Figure 1 ABR diagnostic equipment 126 and / or Figure 2 The ABR diagnostic device 226) is an assistive hearing device configured based on the ABR threshold intensity.

[0059] The blocks included in the described example method 600 are for illustrative purposes. In some embodiments, these blocks may be executed in a different order. In some other embodiments, various blocks may be eliminated. In still other embodiments, individual blocks may be divided into additional blocks, supplemented with other blocks, or combined together to form fewer blocks. Other variations of these particular blocks are contemplated, including changes in the order of the blocks, changes in the content of blocks that are divided or combined into other blocks, etc.

[0060] Example:

[0061] Figures 7 to 9 The following examples described are illustrative and are not intended to limit the scope of this application.

[0062] Figure 7 Examples of using Figures 1 to 6 Example graph 700 compares the methods described in [reference] (e.g., the "XCsubB" method) with the Suthakar-Liberman method (see Hear Res. 2019 Sep 15; 381:107782. Doi: 10.1016 / j.heares.2019.107782.Epub 2019 Aug 8, PMID: 31437652; PMCID: PMC6726521) for ABR threshold strength determination using a common dataset. For the common dataset being tested, heatmap 710 indicates the difference between human classification and the XCsubB method, and heatmap 720 indicates the difference between human classification and the Suthakar-Liberman method. Table 730 indicates the accuracy of the XCsubB method and the Suthakar-Liberman method for a given dataset (e.g., within + / - 7.5 dB and + / - 12.5 dB for human classification), and the percentage of failure for each method. As shown in the figure, the XCsubB method is much more accurate than the Suthakar-Liberman method, and has a much lower failure rate.

[0063] Figure 8 An example is shown using a general dataset with 1950 thresholds across 3 strains, 12 genotypes, and 8 frequencies (e.g., 4,000–45,200 Hz) in manual classification and application. Figures 1 to 6 Example graph 800 compares the ABR threshold strength determinations of the methods described herein (e.g., the "XCsubB" method). For the general dataset being tested, scatter plot 810 indicates the difference between human classification and the XCsubB method, and heatmap 820 indicates the difference between human classification and the XCsubB method. As shown, the XCsubB method is closely correlated with human classification.

[0064] Figure 9 This example illustrates manual classification and its application using a general acoustic cross dataset. Figures 1 to 6 Example chart 900 compares the ABR threshold strength determinations of the methods described herein (e.g., the "XCsubB" method). For the general dataset being tested, chart 910 indicates human classification, and chart 920 indicates XCsubB classification. As shown, the XCsubB method matches human classification very well.

[0065] Although methods and systems have been described in conjunction with preferred embodiments and specific examples, they are not intended to limit the scope to the specific embodiments illustrated, as the embodiments herein are intended to be illustrative rather than restrictive in all respects.

[0066] Unless otherwise expressly stated, it is not intended to interpret any method described herein as requiring its steps to be performed in a particular order. Therefore, no inference is made in any respect of the order in which the method claims do not actually describe the order of their steps, or where the claims or specification do not expressly restrict these steps to a particular order. This applies to any possible non-explicit basis of interpretation, including: logical questions concerning the arrangement of steps or operational procedures; simple meanings derived from grammatical organization or punctuation; and the number or type of embodiments described in the specification.

[0067] Those skilled in the art will recognize, or be able to determine, many equivalents of specific embodiments of the methods and compositions described herein using only conventional experiments. Such equivalents are intended to be covered by the following claims.

Claims

1. A method, the method comprising: At the computing device, multiple ABR waveform samples are received for at least two of the multiple intensities emitted during the auditory brainstem response (ABR) test. For at least two of the plurality of intensities, a respective cross-correlation distribution comprising a plurality of cross-correlation samples is generated in the following manner: Randomly select multiple ABR waveform samples from each of the first groups to assign to the first subset; The remaining groups of each of the multiple ABR waveform samples are assigned to the second subset; Calculate the first median ABR waveform based on the first subset; Calculate the second median ABR waveform based on the second subset; as well as Calculate the cross-correlation between the first median ABR waveform and the second median ABR waveform to generate cross-correlation samples from the plurality of cross-correlation samples; and Based on the plurality of cross-correlation samples, for at least two of the plurality of intensities, calculate the mean of each of the respective cross-correlation distributions; as well as The ABR threshold intensity among the plurality of intensities is identified by comparing the mean of at least two of the intensities with 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 strength by: Fit the mean values ​​of at least two of the plurality of intensities to an S-shaped curve; and Identify the points where the threshold criterion crosses the S-shaped curve.

5. The method of claim 1, further comprising identifying the ABR threshold strength by: Fit the mean values ​​of at least two of the plurality of intensities to a power-law curve; and Identify the locations where the threshold criterion crosses 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, transmitting an audio stimulus signal having an intensity of one of the plurality of intensities via a sound transducer.

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

9. A method, the method comprising: At the computing device, multiple ABR waveform samples are received for at least two of the multiple intensities emitted during the auditory brainstem response (ABR) test. For at least two of the plurality of intensities, a respective cross-correlation distribution is generated, the respective cross-correlation distribution comprising a plurality of cross-correlation samples generated by iterative bootstrap sample signal processing of the respective plurality of ABR waveform samples; Based on the plurality of cross-correlation samples, for at least two of the plurality of intensities, calculate the mean of each of the respective cross-correlation distributions; as well as The ABR threshold intensity among the plurality of intensities is identified by comparing the mean of each of at least two of the plurality of intensities with a threshold criterion.

10. The method of claim 9, further comprising calculating cross-correlated samples among the plurality of cross-correlated samples by measuring the cross-correlation between a first set of randomly selected samples from the respective plurality of ABR waveform samples and the remaining sets of the respective plurality of ABR waveform samples.

11. The method of claim 10, further comprising calculating the cross-correlation sample among the plurality of cross-correlation samples by measuring the cross-correlation between a first median ABR waveform generated from a first set of randomly selected samples from the respective plurality of ABR waveform samples and a second median ABR waveform generated from the remaining set of the respective 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: Fit the mean values ​​of at least two of the plurality of intensities to an S-shaped curve; and Identify the points where the threshold criterion crosses the S-shaped curve.

15. The method of claim 9, further comprising identifying the ABR threshold intensity by: Fit the mean values ​​of at least two of the plurality of intensities to a power-law curve; and Identify the locations where the threshold criterion crosses 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, transmitting an audio stimulus signal having an intensity of one of the plurality of intensities via a sound transducer.

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

19. A non-transitory computer-readable medium storing processor-executable instructions, which, when executed by at least one processor, cause the at least one processor to: For at least two sound intensities emitted during the auditory brainstem response (ABR) test, multiple ABR waveform samples are received for each of the following: For at least two of the plurality of intensities, a respective cross-correlation distribution comprising a plurality of cross-correlation samples is generated in the following manner: Randomly select multiple ABR waveform samples from each of the first groups to assign to the first subset; The remaining groups of each of the multiple ABR waveform samples are assigned to the second subset; Calculate the first median ABR waveform based on the first subset; Calculate the second median ABR waveform based on the second subset; as well as Calculate the cross-correlation between the first median ABR waveform and the second median ABR waveform to generate cross-correlation samples from the plurality of cross-correlation samples; and Based on the plurality of cross-correlation samples, for at least two of the plurality of intensities, calculate the mean of each of the respective cross-correlation distributions; as well as The ABR threshold intensity among the plurality of intensities is identified by comparing the mean of at least two of the intensities with 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 strength.

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 instruction that causes the at least one processor to recognize the ABR threshold strength, when executed by the at least one processor, further causes the at least one processor to: Fit the mean values ​​of at least two of the plurality of intensities to an S-shaped curve; and Identify the points where the threshold criterion crosses the S-shaped curve.

23. The non-transitory computer-readable medium of claim 19, wherein the processor-executable instruction that causes the at least one processor to recognize the ABR threshold strength, when executed by the at least one processor, further causes the at least one processor to: Fit the mean values ​​of at least two of the plurality of intensities to a power-law curve; and Identify the locations where the threshold criterion crosses 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 the 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 transmit an audio stimulus signal having an intensity of one of the plurality of intensities via a sound transducer during the ABR test.

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 receive, during the ABR test, each of the plurality of ABR waveform samples corresponding to the intensity of the plurality of intensities via a pair of electrodes.

27. A non-transitory computer-readable medium storing processor-executable instructions, which, when executed by at least one processor, cause the at least one processor to: For at least two of the multiple intensities emitted during the auditory brainstem response (ABR) test, multiple ABR waveform samples are received for each of the following: For at least two of the plurality of intensities, a respective cross-correlation distribution is generated, the respective cross-correlation distribution comprising a plurality of cross-correlation samples generated by iterative bootstrap sample signal processing of the respective plurality of ABR waveform samples; Based on the plurality of cross-correlation samples, for at least two of the plurality of intensities, calculate the mean of each of the respective cross-correlation distributions; as well as The ABR threshold intensity among the plurality of intensities is identified by comparing the mean of each of at least two of the plurality of intensities with 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 cross-correlation samples among the plurality of cross-correlation samples by measuring the cross-correlation between a first set of randomly selected samples from the respective plurality of ABR waveform samples and the remaining sets of the respective 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 among the plurality of cross-correlation samples by measuring the cross-correlation between a first median ABR waveform generated from a first set of randomly selected samples from the respective plurality of ABR waveform samples and a second median ABR waveform generated from the remaining set of the respective 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 strength.

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 instruction that causes the at least one processor to recognize the ABR threshold strength, when executed by the at least one processor, further causes the at least one processor to: Fit the mean values ​​of at least two of the plurality of intensities to an S-shaped curve; and Identify the points where the threshold criterion crosses the S-shaped curve.

33. The non-transitory computer-readable medium of claim 27, wherein the processor-executable instruction that causes the at least one processor to recognize the ABR threshold strength, when executed by the at least one processor, further causes the at least one processor to: Fit the mean values ​​of at least two of the plurality of intensities to a power-law curve; and Identify the locations where the threshold criterion crosses 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 cause the at least one processor to configure the 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 transmit an audio stimulus signal having an intensity of one of the plurality of intensities via a sound transducer during the ABR test.

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 receive, during the ABR test, each of the plurality of ABR waveform samples corresponding to the intensity of the plurality of intensities via a pair of electrodes.

37. A system comprising: A first computing device, the first computing device being configured to: For at least two sound intensities emitted during the auditory brainstem response (ABR) test, multiple ABR waveform samples are received for each of the following: For at least two of the plurality of intensities, a respective cross-correlation distribution comprising a plurality of cross-correlation samples is generated in the following manner: Randomly select multiple ABR waveform samples from each of the first groups to assign to the first subset; The remaining groups of each of the multiple ABR waveform samples are assigned to the second subset; Calculate the first median ABR waveform based on the first subset; Calculate the second median ABR waveform based on the second subset; as well as Calculate the cross-correlation between the first median ABR waveform and the second median ABR waveform to generate cross-correlation samples from the plurality of cross-correlation samples; and Based on the plurality of cross-correlation samples, for at least two of the plurality of intensities, calculate the mean of each of the respective cross-correlation distributions; as well as The ABR threshold intensity among the plurality of intensities is identified by comparing the mean of at least two of the intensities with a threshold criterion. as well as A second computing device is configured to receive the identified ABR threshold strength.

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 strength comprises the first computing device further configured to: Fit the mean values ​​of at least two of the plurality of intensities to an S-shaped curve; and Identify the points where the threshold criterion crosses the S-shaped curve.

41. The system of claim 37, wherein the first computing device configured to identify the ABR threshold strength comprises the first computing device further configured to: Fit the mean values ​​of at least two of the plurality of intensities to a power-law curve; and Identify the locations where the threshold criterion crosses the power-law curve.

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

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

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

45. A system comprising: A first computing device, the first computing device being configured to: For at least two of the multiple intensities emitted during the auditory brainstem response (ABR) test, multiple ABR waveform samples are received for each of the following: For at least two of the plurality of intensities, a respective cross-correlation distribution is generated, the respective cross-correlation distribution comprising a plurality of cross-correlation samples generated by iterative bootstrap sample signal processing of the respective plurality of ABR waveform samples; Based on the plurality of cross-correlation samples, for at least two of the plurality of intensities, calculate the mean of each of the respective cross-correlation distributions; as well as The ABR threshold intensity among the plurality of intensities is identified by comparing the mean of each of at least two of the plurality of intensities with a threshold criterion. as well as A second computing device is configured to receive the identified ABR threshold strength.

46. ​​The system of claim 45, wherein the first computing device is further configured to calculate cross-correlated samples among the plurality of cross-correlated samples by measuring the cross-correlation between a first set of randomly selected samples from the respective plurality of ABR waveform samples and the remaining sets of the respective 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 among the plurality of cross-correlation samples by measuring the cross-correlation between a first median ABR waveform generated from a first set of randomly selected samples from the respective plurality of ABR waveform samples and a second median ABR waveform generated from the remaining set of the respective 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 strength comprises the first computing device further configured to: Fit the mean values ​​of at least two of the plurality of intensities to an S-shaped curve; and Identify the points where the threshold criterion crosses the S-shaped curve.

51. The system of claim 45, wherein the first computing device configured to identify the ABR threshold strength comprises the first computing device further configured to: Fit the mean values ​​of at least two of the plurality of intensities to a power-law curve; and Identify the locations where the threshold criterion crosses the power-law curve.

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

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

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

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