Prediction of the Functional Status of the Cochlear Nerve in Individual Cochlear Implant Users Using Machine Learning and Electrophysiological Measures

Predicting Acoustic Hearing Preservation Following Cochlear Implant Surgery Using Machine Learning

OBJECTIVES

The aim of the study was to train and test supervised machine-learning classifiers to predict acoustic hearing preservation after CI using preoperative clinical data.

STUDY DESIGN

Retrospective predictive modeling study of prospectively collected single-institution CI dataset.

METHODS

One hundred and seventy-five patients from a REDCap database including 761 patients >18 years who underwent CI and had audiometric testing preoperatively and one month after surgery were included. The primary outcome variable was the lowest quartile change in acoustic hearing at one month after CI using various formulae (standard pure tone average, SPTA; low-frequency PTA, LFPTA). Analysis involved applying multivariate logistic regression to detect statistical associations and training and testing supervised learning classifiers. Classifier performance was assessed with numerous metrics including area under the receiver operating characteristic curve (AUC) and Matthews correlation coefficient (MCC).

RESULTS

Lowest quartile change (indicating hearing preservation) in SPTA was positively associated with a history of meningitis, preoperative LFPTA, and preoperative SPTA. Lowest quartile change in SPTA was negatively associated with sudden hearing loss, noise exposure, aural fullness, and abnormal anatomy. Lowest quartile change in LFPTA was positively associated with preoperative LFPTA. Lowest quartile change in LFPTA was negatively associated with tobacco use. Random forest demonstrated the highest mean classification performance on the validation dataset when predicting each of the outcome variables.

CONCLUSIONS

Machine learning demonstrated utility for predicting preservation of residual acoustic hearing in patients undergoing CI surgery, and the detected associations facilitated the interpretation of our machine-learning models. The models and statistical associations together may be used to facilitate improvements in shared clinical decision-making and patient outcomes.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:926-936, 2024.

Insights Into Electrophysiological Metrics of Cochlear Health in Cochlear Implant Users Using a Computational Model

PURPOSE

The hearing outcomes of cochlear implant users depend on the functional status of the electrode-neuron interface inside the cochlea. This can be assessed by measuring electrically evoked compound action potentials (eCAPs). Variations in cochlear neural health and survival are reflected in eCAP-based metrics. The difficulty in translating promising results from animal studies into clinical use has raised questions about to what degree eCAP-based metrics are influenced by non-neural factors. Here, we addressed these questions using a computational model.

METHODS

A 2-D computational model was designed to simulate how electrical signals from the stimulating electrode reach the auditory nerve fibers distributed along the cochlea, evoking action potentials that can be recorded as compound responses at the recording electrodes. Effects of physiologically relevant variations in neural survival and in electrode-neuron and stimulating-recording electrode distances on eCAP amplitude growth functions (AGFs) were investigated.

RESULTS

In line with existing literature, the predicted eCAP AGF slopes and the inter-phase gap (IPG) effects depended on the neural survival, but only when the IPG effect was calculated as the difference between the slopes of the two AGFs expressed in linear input-output scale. As expected, shallower eCAP AGF slopes were obtained for increased stimulating-recording electrode distance and larger eCAP thresholds for greater electrode-neuron distance. These non-neural factors had also minor interference on the predicted IPG effect.

CONCLUSIONS

The model predictions demonstrate previously found dependencies of eCAP metrics on neural survival and non-neural aspects. The present findings confirm data from animal studies and provide insights into applying described metrics in clinical practice.

A new method for removing artifacts from recordings of the electrically evoked compound action potential: Single-pulse stimulation

This report presents a new method for removing electrical artifact contamination from the electrically evoked compound action potential (eCAP) evoked by single cathodic-leading, biphasic-pulse stimulation. The development of the new method is motivated by results recorded in human cochlear implant (CI) users showing that the fundamental assumption of the classic forward masking artifact rejection technique is violated in up to 45% of cases tested at high stimulation levels when using default stimulation parameters. Subsequently, the new method developed based on the discovery that a hyperbola best characterizes the artifacts created during stimulation and recording is described. The eCAP waveforms obtained using the new method are compared to those recorded using the classic forward masking technique. The results show that eCAP waveforms obtained using both methods are comparable when the fundamental assumption of the classic forward masking technique is met. In contrast, eCAP amplitudes obtained using the two methods are significantly different when the fundamental assumption of the classic forward masking technique is violated, with greater differences in the eCAP amplitude for greater assumption violations. The new method also has excellent test-retest reliability (Intraclass correlation > 0.98). Overall, the new method is a viable alternative to the classic forward masking technique for obtaining artifact-free eCAPs evoked by single-pulse stimulation in CI users.

The Relationship Between Cochlear Implant Speech Perception Outcomes and Electrophysiological Measures of the Electrically Evoked Compound Action Potential

OBJECTIVE

This study assessed the relationship between electrophysiological measures of the electrically evoked compound action potential (eCAP) and speech perception scores measured in quiet and in noise in postlingually deafened adult cochlear implant (CI) users. It tested the hypothesis that how well the auditory nerve (AN) responds to electrical stimulation is important for speech perception with a CI in challenging listening conditions.

DESIGN

Study participants included 24 postlingually deafened adult CI users. All participants used Cochlear Nucleus CIs in their test ears. In each participant, eCAPs were measured at multiple electrode locations in response to single-pulse, paired-pulse, and pulse-train stimuli. Independent variables included six metrics calculated from the eCAP recordings: the electrode-neuron interface (ENI) index, the neural adaptation (NA) ratio, NA speed, the adaptation recovery (AR) ratio, AR speed, and the amplitude modulation (AM) ratio. The ENI index quantified the effectiveness of the CI electrodes in stimulating the targeted AN fibers. The NA ratio indicated the amount of NA at the AN caused by a train of constant-amplitude pulses. NA speed was defined as the speed/rate of NA. The AR ratio estimated the amount of recovery from NA at a fixed time point after the cessation of pulse-train stimulation. AR speed referred to the speed of recovery from NA caused by previous pulse-train stimulation. The AM ratio provided a measure of AN sensitivity to AM cues. Participants' speech perception scores were measured using Consonant-Nucleus-Consonant (CNC) word lists and AzBio sentences presented in quiet, as well as in noise at signal-to-noise ratios (SNRs) of +10 and +5 dB. Predictive models were created for each speech measure to identify eCAP metrics with meaningful predictive power.

RESULTS

The ENI index and AR speed individually explained at least 10% of the variance in most of the speech perception scores measured in this study, while the NA ratio, NA speed, the AR ratio, and the AM ratio did not. The ENI index was identified as the only eCAP metric that had unique predictive power for each of the speech test results. The amount of variance in speech perception scores (both CNC words and AzBio sentences) explained by the eCAP metrics increased with increased difficulty under the listening condition. Over half of the variance in speech perception scores measured in +5 dB SNR noise (both CNC words and AzBio sentences) was explained by a model with only three eCAP metrics: the ENI index, NA speed, and AR speed.

CONCLUSIONS

Of the six electrophysiological measures assessed in this study, the ENI index is the most informative predictor for speech perception performance in CI users. In agreement with the tested hypothesis, the response characteristics of the AN to electrical stimulation are more important for speech perception with a CI in noise than they are in quiet.

Electrically evoked compound action potentials in cochlear implant users with preoperative residual hearing

Introduction

Residual hearing in cochlear implant (CI) candidates requires the functional integrity of the nerve in particular regions of the cochlea. Nerve activity can be elicited as electrically evoked compound action potentials (ECAP) after cochlear implantation. We hypothesize that ECAP thresholds depend on preoperative residual hearing ability.

Materials and methods

In a retrospective study, we analyzed 84 adult cochlear implant users who had received a Nucleus® CI632 Slim Modiolar Electrode and who preoperatively had had residual hearing. Inclusion criteria were severe to profound hearing loss with preoperative measurable hearing in the ear to receive the implant, postlingual hearing loss, German as native language and correct placement of the electrode, inserted completely into the scala tympani. Electrically evoked compound action potential (ECAP) was recorded intraoperatively. The angular insertion was measured for each electrode contact from postoperative computed tomography to estimate the corresponding spiral ganglion frequency. Pure-tone audiometry and allocated ECAP thresholds were tested to investigate possible correlation.

Results

The average of hearing thresholds, tested at 0.5, 1, 2, and 4 kHz (4FPTA) was 82 ± 18 (range 47-129) dB HL. The success rate for recording ECAP thresholds was 96.9%. For all comparable pure-tone frequencies (1, 2, 4, and 8 kHz), there was significant correlation between preoperative hearing levels and intraoperative ECAP thresholds (p < 0.001). Higher hearing thresholds are associated with increased ECAP thresholds.

Conclusion

In CI candidates with adequate residual hearing, intraoperative electrophysiological measurement records lower thresholds. This outcome may be explained by the neural survival density of the peripheral system, with less neural degeneration.

Cochlear implantation programming characteristics and outcomes of cochlear nerve deficiency

PURPOSE

Due to the specificity of cochlear implantation (CI) programming parameters and outcomes in cochlear nerve deficiency (CND) patients, this study aimed to investigate the correlation between programming parameters and outcomes and further compare the difference between normal and CND groups.

METHODS

Ninety (95 ears) CND patients (normal cochlea, 39; malformed cochlea, 56) and seventy-nine (81 ears) normal cochlea patients who underwent CI surgery with either Med-El or Cochlear devices were included. The programming parameters and outcomes evaluated by the questionnaires were collected and compared among the normal CND, malformed CND, and normal groups in the two device groups, and their correlation was analyzed.

RESULTS

In the CND group, a reduced stimulation rate, higher pulse width, and triphasic pulse were needed in some cases. The stimulus levels of the CND group were significantly higher than that of the normal group (p < 0.05), but the outcomes of the CND group were significantly worse than that of the normal group (p < 0.05), and the stimulus level was significantly correlated with the outcomes (p < 0.05). However, there was no difference between normal and malformed CND groups. The non-auditory response was observed in the CND group, especially the ones with malformations.

CONCLUSION

The CI programming parameters of some CND patients need to be adjusted, and a slower stimulation rate and higher pulse width are required sometimes. CND patients need a higher stimulus level than normal patients but their outcomes are poorer. Non-auditory response should be noticed in CND patients during programming.

Artificial Intelligence and Machine (Deep) Learning in Otorhinolaryngology: A Bibliometric Analysis Based on VOSviewer and CiteSpace

BACKGROUND

Otorhinolaryngology diseases are well suited for artificial intelligence (AI)-based interpretation. The use of AI, particularly AI based on deep learning (DL), in the treatment of human diseases is becoming more and more popular. However, there are few bibliometric analyses that have systematically studied this field.

OBJECTIVE

The objective of this study was to visualize the research hot spots and trends of AI and DL in ENT diseases through bibliometric analysis to help researchers understand the future development of basic and clinical research.

METHODS

In all, 232 articles and reviews were retrieved from The Web of Science Core Collection. Using CiteSpace and VOSviewer software, countries, institutions, authors, references, and keywords in the field were visualized and examined.

RESULTS

The majority of these papers came from 44 nations and 498 institutions, with China and the United States leading the way. Common diseases used by AI in ENT include otosclerosis, otitis media, nasal polyps, sinusitis, and so on. In the early years, research focused on the analysis of hearing and articulation disorders, and in recent years mainly on the diagnosis, localization, and grading of diseases.

CONCLUSIONS

The analysis shows the periodical hot spots and development direction of AI and DL application in ENT diseases from the time dimension. The diagnosis and prognosis of otolaryngology diseases and the analysis of otolaryngology endoscopic images have been the focus of current research and the development trend of future.

Towards personalized and optimized fitting of cochlear implants

A cochlear implant (CI) is a neurotechnological device that restores total sensorineural hearing loss. It contains a sophisticated speech processor that analyzes and transforms the acoustic input. It distributes its time-enveloped spectral content to the auditory nerve as electrical pulsed stimulation trains of selected frequency channels on a multi-contact electrode that is surgically inserted in the cochlear duct. This remarkable brain interface enables the deaf to regain hearing and understand speech. However, tuning of the large (>50) number of parameters of the speech processor, so-called "device fitting," is a tedious and complex process, which is mainly carried out in the clinic through 'one-size-fits-all' procedures. Current fitting typically relies on limited and often subjective data that must be collected in limited time. Despite the success of the CI as a hearing-restoration device, variability in speech-recognition scores among users is still very large, and mostly unexplained. The major factors that underly this variability incorporate three levels: (i) variability in auditory-system malfunction of CI-users, (ii) variability in the selectivity of electrode-to-auditory nerve (EL-AN) activation, and (iii) lack of objective perceptual measures to optimize the fitting. We argue that variability in speech recognition can only be alleviated by using objective patient-specific data for an individualized fitting procedure, which incorporates knowledge from all three levels. In this paper, we propose a series of experiments, aimed at collecting a large amount of objective (i.e., quantitative, reproducible, and reliable) data that characterize the three processing levels of the user's auditory system. Machine-learning algorithms that process these data will eventually enable the clinician to derive reliable and personalized characteristics of the user's auditory system, the quality of EL-AN signal transfer, and predictions of the perceptual effects of changes in the current fitting.

Responsiveness of the electrically stimulated cochlear nerve in patients with a missense variant in ACTG1: Preliminary Results

This preliminary study identified a missense variant in ACTG1 (NM_001614.5) in a family with autosomal dominant non-syndromic hearing loss (ADNSHL). The responsiveness of the electrically-stimulated cochlear nerve (CN) in two implanted participants with this missense change was also evaluated and reported. Genetic testing was done using a custom capture panel (MiamiOtoGenes) and whole exome sequencing. The responsiveness of the electrically-stimulated CN was evaluated in two members of this family (G1 and G4) using the electrically evoked compound action potential (eCAP). eCAP results from these two participants were compared with those measured three implanted patient populations: children with cochlear nerve deficiency, children with idiopathic hearing loss and normal-sized cochlear nerves, and postligually deafened adults. Sequencing of ACTG1 identified a missense c.737A>T (p. Gln246Leu) variant in ACTG1 (NM_001614.5) which is most likely the genetic cause of ADNSHL in this family. eCAP results measured in these two participants showed substantial variations. The results indicated the missense c.737A>T (p. Gln246Leu) variant in ACTG1 (NM_001614.5) co-segregated with hearing loss in this family. The responsiveness of the electrically-stimulated CN can vary among patients with the same genetic variants, which suggests the importance of evaluating the functional status of the CN for individual CI patients.

Prediction of hearing preservation after acoustic neuroma surgery based on SMOTE-XGBoost

Prior to the surgical removal of an acoustic neuroma, the majority of patients anticipate that their hearing will be preserved to the greatest possible extent following surgery. This paper proposes a postoperative hearing preservation prediction model for the characteristics of class-imbalanced hospital real data based on the extreme gradient boost tree (XGBoost). In order to eliminate sample imbalance, the synthetic minority oversampling technique (SMOTE) is applied to increase the number of underclass samples in the data. Multiple machine learning models are also used for the accurate prediction of surgical hearing preservation in acoustic neuroma patients. In comparison to research results from existing literature, the experimental results found the model proposed in this paper to be superior. In summary, the method this paper proposes can make a significant contribution to the development of personalized preoperative diagnosis and treatment plans for patients, leading to effective judgment for the hearing retention of patients with acoustic neuroma following surgery, a simplified long medical treatment process and saved medical resources.

Relationships Between the Auditory Nerve Sensitivity to Amplitude Modulation, Perceptual Amplitude Modulation Rate Discrimination Sensitivity, and Speech Perception Performance in Postlingually Deafened Adult Cochlear Implant Users

OBJECTIVE

This study assessed the relationships between the salience of amplitude modulation (AM) cues encoded at the auditory nerve (AN), perceptual sensitivity to changes in AM rate (i.e., AM rate discrimination threshold, AMRDT), and speech perception scores in postlingually deafened adult cochlear implant (CI) users.

DESIGN

Study participants were 18 postlingually deafened adults with Cochlear Nucleus devices, including five bilaterally implanted patients. For each of 23 implanted ears, neural encoding of AM cues at 20 Hz at the AN was evaluated at seven electrode locations across the electrode array using electrophysiological measures of the electrically evoked compound action potential (eCAP). The salience of AM neural encoding was quantified by the Modulated Response Amplitude Ratio (MRAR). Psychophysical measures of AMRDT for 20 Hz modulation were evaluated in 16 ears using a three-alternative, forced-choice procedure, targeting 79.4% correct on the psychometric function. AMRDT was measured at up to five electrode locations for each test ear, including the electrode pair that showed the largest difference in the MRAR. Consonant-Nucleus-Consonant (CNC) word scores presented in quiet and in speech-shaped noise at a signal to noise ratio (SNR) of +10 dB were measured in all 23 implanted ears. Simulation tests were used to assess the variations in correlation results when using the MRAR and AMRDT measured at only one electrode location in each participant to correlate with CNC word scores. Linear Mixed Models (LMMs) were used to evaluate the relationship between MRARs/AMRDTs measured at individual electrode locations and CNC word scores. Spearman Rank correlation tests were used to evaluate the strength of association between CNC word scores measured in quiet and in noise with (1) the variances in MRARs and AMRDTs, and (2) the averaged MRAR or AMRDT across multiple electrodes tested for each participant.

RESULTS

There was no association between the MRAR and AMRDT. Using the MRAR and AMRDT measured at only one, randomly selected electrode location to assess their associations with CNC word scores could lead to opposite conclusions. Both the results of LMMs and Spearman Rank correlation tests showed that CNC word scores measured in quiet or at 10 dB SNR were not significantly correlated with the MRAR or AMRDT. In addition, the results of Spearman Rank correlation tests showed that the variances in MRARs and AMRDTs were not significantly correlated with CNC word scores measured in quiet or in noise.

CONCLUSIONS

The difference in AN sensitivity to AM cues is not the primary factor accounting for the variation in AMRDTs measured at different stimulation sites within individual CI users. The AN sensitivity to AM per se may not be a crucial factor for CNC word perception in quiet or at 10 dB SNR in postlingually deafened adult CI users. Using electrophysiological or psychophysical results measured at only one electrode location to correlate with speech perception scores in CI users can lead to inaccurate, if not wrong, conclusions.

Comparison of response properties of the electrically stimulated auditory nerve reported in human listeners and in animal models

Cochlear implants (CIs) provide acoustic information to implanted patients by electrically stimulating nearby auditory nerve fibers (ANFs) which then transmit the information to higher-level neural structures for further processing and interpretation. Computational models that simulate ANF responses to CI stimuli enable the exploration of the mechanisms underlying CI performance beyond the capacity of in vivo experimentation alone. However, all ANF models developed to date utilize to some extent anatomical/morphometric data, biophysical properties and/or physiological data measured in non-human animal models. This review compares response properties of the electrically stimulated auditory nerve (AN) in human listeners and different mammalian models. Properties of AN responses to single pulse stimulation, paired-pulse stimulation, and pulse-train stimulation are presented. While some AN response properties are similar between human listeners and animal models (e.g., increased AN sensitivity to single pulse stimuli with long interphase gaps), there are some significant differences. For example, the AN of most animal models is typically more sensitive to cathodic stimulation while the AN of human listeners is generally more sensitive to anodic stimulation. Additionally, there are substantial differences in the speed of recovery from neural adaptation between animal models and human listeners. Therefore, results from animal models cannot be simply translated to human listeners. Recognizing the differences in responses of the AN to electrical stimulation between humans and other mammals is an important step for creating ANF models that are more applicable to various human CI patient populations.

Machine Learning-Based Prediction of the Outcomes of Cochlear Implantation in Patients With Cochlear Nerve Deficiency and Normal Cochlea: A 2-Year Follow-Up of 70 Children

Cochlear nerve deficiency (CND) is often associated with variable outcomes of cochlear implantation (CI). We assessed previous investigations aiming to identify the main factors that determine CI outcomes, which would enable us to develop predictive models. Seventy patients with CND and normal cochlea who underwent CI surgery were retrospectively examined. First, using a data-driven approach, we collected demographic information, radiographic measurements, audiological findings, and audition and speech assessments. Next, CI outcomes were evaluated based on the scores obtained after 2 years of CI from the Categories of Auditory Performance index, Speech Intelligibility Rating, Infant/Toddler Meaningful Auditory Integration Scale or Meaningful Auditory Integration Scale, and Meaningful Use of Speech Scale. Then, we measured and averaged the audiological and radiographic characteristics of the patients to form feature vectors, adopting a multivariate feature selection method, called stability selection, to select the features that were consistent within a certain range of model parameters. Stability selection analysis identified two out of six characteristics, namely the vestibulocochlear nerve (VCN) area and the number of nerve bundles, which played an important role in predicting the hearing and speech rehabilitation results of CND patients. Finally, we used a parameter-optimized support vector machine (SVM) as a classifier to study the postoperative hearing and speech rehabilitation of the patients. For hearing rehabilitation, the accuracy rate was 71% for both the SVM classification and the area under the curve (AUC), whereas for speech rehabilitation, the accuracy rate for SVM classification and AUC was 93% and 94%, respectively. Our results identified that a greater number of nerve bundles and a larger VCN area were associated with better CI outcomes. The number of nerve bundles and VCN area can predict CI outcomes in patients with CND. These findings can help surgeons in selecting the side for CI and provide reasonable expectations for the outcomes of CI surgery.

Value of Preoperative Imaging Results in Predicting Cochlear Nerve Function in Children Diagnosed With Cochlear Nerve Aplasia Based on Imaging Results

This study aimed to assess the function of the cochlear nerve using electrically evoked compound action potentials (ECAPs) for children with cochlear implants who were diagnosed with cochlear nerve aplasia and to analyze the correlation between preimplantation imaging results and ECAP responses. Thirty-five children diagnosed with cochlear nerve aplasia based on magnetic resonance imaging (MRI) were included. Preimplantation MRI and high-resolution computed tomography (HRCT) images were reconstructed, and the width of the bone cochlear nerve canal (BCNC), the diameter of the vestibulocochlear nerve (VCN), and the diameter of the facial nerve (FN) were measured. ECAP input/output (I/O) functions were measured at three electrode locations along the electrode array for each participant. The relationship between ECAP responses (including ECAP threshold, ECAP maximum amplitude, and slope of ECAP I/O function) and sizes of the BCNC and VCN was analyzed using Pearson's correlation coefficients. Our analysis revealed that ECAP responses varied greatly among individual participants. Overall, ECAP thresholds gradually increased, while maximum amplitudes and ECAP I/O function slopes gradually decreased, as the electrode location moved from the basal to the apical direction in the cochlea. ECAP responses exhibited no significant correlations with BCNC width or VCN diameter. The ratio of the VCN to FN diameters was significantly correlated with the slope of the ECAP I/O function and the maximum amplitude. BCNC width could not predict the function of the cochlear nerve. Compared with the absolute size of the VCN, the size of the VCN relative to the FN may represent an indicator for predicting the functional status of the cochlear nerve in children diagnosed with cochlear nerve aplasia based on imaging results.

Interpreting the interphase gap effect on the electrically evoked compound action potential

This study demonstrated the effects of using different quantification methods and parameter scales on the sensitivity of the electrically evoked compound action potential (eCAP) to changes in the interphase gap (IPG). The IPG effect measured in two groups of cochlear implant (CI) users with different cochlear nerve (CN) health on seven eCAP measures was quantified using an absolute and a proportional difference method. The IPG effect provides an indicator for the functional status of the CN in human CI users. Specifying how the IPG effect is quantified is critical for accurate result interpretation.

The Effect of Advanced Age on the Electrode-Neuron Interface in Cochlear Implant Users

OBJECTIVES

This study aimed to determine the effect of advanced age on how effectively a cochlear implant (CI) electrode stimulates the targeted cochlear nerve fibers (i.e., the electrode-neuron interface [ENI]) in postlingually deafened adult CI users. The study tested the hypothesis that the quality of the ENI declined with advanced age. It also tested the hypothesis that the effect of advanced age on the quality of the ENI would be greater in basal regions of the cochlea compared to apical regions.

DESIGN

Study participants included 40 postlingually deafened adult CI users. The participants were separated into two age groups based on age at testing in accordance with age classification terms used by the World Health Organization and the Medical Literature Analysis and Retrieval System Online bibliographic database. The middle-aged group included 16 participants between the ages of 45 and 64 years and the elderly group included 24 participants older than 65 years. Results were included from one ear for each participant. All participants used Cochlear Nucleus CIs in their test ears. For each participant, electrophysiological measures of the electrically evoked compound action potential (eCAP) were used to measure refractory recovery functions and amplitude growth functions (AGFs) at three to seven electrode sites across the electrode array. The eCAP parameters used in this study included the refractory recovery time estimated based on the eCAP refractory recovery function, the eCAP threshold, the slope of the eCAP AGF, and the negative-peak (i.e., N1) latency. The electrode-specific ENI was evaluated using an optimized combination of the eCAP parameters that represented the responsiveness of cochlear nerve fibers to electrical stimulation delivered by individual electrodes along the electrode array. The quality of the electrode-specific ENI was quantified by the local ENI index, a value between 0 and 100 where 0 and 100 represented the lowest- and the highest-quality ENI across all participants and electrodes in the study dataset, respectively.

RESULTS

There were no significant age group differences in refractory times, eCAP thresholds, N1 latencies or local ENI indices. Slopes of the eCAP AGF were significantly larger in the middle-aged group compared to the elderly group. There was a significant effect of electrode location on each eCAP parameter, except for N1 latency. In addition, the local ENI index was significantly larger (i.e., better ENI) in the apical region than in the basal and middle regions of the cochlea for both age groups.

CONCLUSIONS

The model developed in this study can be used to estimate the quality of the ENI at individual electrode locations in CI users. The quality of the ENI is affected by the location of the electrode along the length of the cochlea. The method for estimating the quality of the ENI developed in this study holds promise for identifying electrodes with poor ENIs that could be deactivated from the clinical programming map. The ENI is not strongly affected by advanced age in middle-aged and elderly CI users.

A Broadly Applicable Method for Characterizing the Slope of the Electrically Evoked Compound Action Potential Amplitude Growth Function

OBJECTIVES

Amplitudes of electrically evoked compound action potentials (eCAPs) as a function of the stimulation level constitute the eCAP amplitude growth function (AGF). The slope of the eCAP AGF (i.e., rate of growth of eCAP amplitude as a function of stimulation level), recorded from subjects with cochlear implants (CIs), has been widely used as an indicator of survival of cochlear nerve fibers. However, substantial variation in the approach used to calculate the slope of the eCAP AGF makes it difficult to compare results across studies. In this study, we developed an improved slope-fitting method by addressing the limitations of previously used approaches and ensuring its application for the estimation of the maximum slopes of the eCAP AGFs recorded in both animal models and human listeners with various etiologies.

DESIGN

The new eCAP AGF fitting method was designed based on sliding window linear regression. Slopes of the eCAP AGF estimated using this new fitting method were calculated and compared with those estimated using four other fitting methods reported in the literature. These four methods were nonlinear regression with a sigmoid function, linear regression, gradient calculation, and boxcar smoothing. The comparison was based on the fitting results of 72 eCAP AGFs recorded from 18 acutely implanted guinea pigs, 46 eCAP AGFs recorded from 23 chronically implanted guinea pigs, and 2094 eCAP AGFs recorded from 200 human CI users from 4 patient populations. The effect of the choice of input units of the eCAP AGF (linear versus logarithmic) on fitting results was also evaluated.

RESULTS

The slope of the eCAP AGF was significantly influenced by the slope-fitting method and by the choice of input units. Overall, slopes estimated using all five fitting methods reflected known patterns of neural survival in human patient populations and were significantly correlated with speech perception scores. However, slopes estimated using the newly developed method showed the highest correlation with spiral ganglion neuron density among all five fitting methods for animal models. In addition, this new method could reliably and accurately estimate the slope for 4 human patient populations, while the performance of the other methods was highly influenced by the morphology of the eCAP AGF.

CONCLUSIONS

The novel slope-fitting method presented in this study addressed the limitations of the other methods reported in the literature and successfully characterized the slope of the eCAP AGF for various animal models and CI patient populations. This method may be useful for researchers in conducting scientific studies and for clinicians in providing clinical care for CI users.