Amplitude growth of intracochlear electrocochleography in cochlear implant users with residual hearing

Intracochlear Recording of Electrocochleography During and After Cochlear Implant Insertion Dependent on the Location in the Cochlea

To preserve residual hearing during cochlear implant (CI) surgery it is desirable to use intraoperative monitoring of inner ear function (cochlear monitoring). A promising method is electrocochleography (ECochG). Within this project the relations between intracochlear ECochG recordings, position of the recording contact in the cochlea with respect to anatomy and frequency and preservation of residual hearing were investigated. The aim was to better understand the changes in ECochG signals and whether these are due to the electrode position in the cochlea or to trauma generated during insertion. During and after insertion of hearing preservation electrodes, intraoperative ECochG recordings were performed using the CI electrode (MED-EL). During insertion, the recordings were performed at discrete insertion steps on electrode contact 1. After insertion as well as postoperatively the recordings were performed at different electrode contacts. The electrode location in the cochlea during insertion was estimated by mathematical models using preoperative clinical imaging, the postoperative location was measured using postoperative clinical imaging. The recordings were analyzed from six adult CI recipients. In the four patients with good residual hearing in the low frequencies the signal amplitude rose with largest amplitudes being recorded closest to the generators of the stimulation frequency, while in both cases with severe pantonal hearing losses the amplitude initially rose and then dropped. This might be due to various reasons as discussed in the following. Our results indicate that this approach can provide valuable information for the interpretation of intracochlearly recorded ECochG signals.

Real-Time Feature Extraction From Electrocochleography With Impedance Measurements During Cochlear Implantation Using Linear State-Space Models

Electrocochleography (ECochG) is increasingly used to monitor the inner ear function of cochlear implant (CI) patients during surgery. Current ECochG-based trauma detection shows low sensitivity and specificity and depends on visual analysis by experts. Trauma detection could be improved by including electric impedance data recorded simultaneously with the ECochG. However, combined recordings are rarely used because the impedance measurements produce artifacts in the ECochG. In this study, we propose a framework for automated real-time analysis of intraoperative ECochG signals using Autonomous Linear State-Space Models (ALSSMs). We developed ALSSM based algorithms for noise reduction, artifact removal, and feature extraction in ECochG. Feature extraction includes local amplitude and phase estimations and a confidence metric over the presence of a physiological response in a recording. We tested the algorithms in a controlled sensitivity analysis using simulations and validated them with real patient data recorded during surgeries. The results from simulation data show that the ALSSM method provides improved accuracy in the amplitude estimation together with a more robust confidence metric of ECochG signals compared to the state-of-the-art methods based on the fast Fourier transform (FFT). Tests with patient data showed promising clinical applicability and consistency with the findings from the simulations. We showed that ALSSMs are a valid tool for real-time analysis of ECochG recordings. Removal of artifacts using ALSSMs enables simultaneous recording of ECochG and impedance data. The proposed feature extraction method provides the means to automate the assessment of ECochG. Further validation of the algorithms in clinical data is needed.

Objective evaluation of intracochlear electrocochleography: repeatability, thresholds, and tonotopic patterns

Introduction

Intracochlear electrocochleography (ECochG) is increasingly being used to measure residual inner ear function in cochlear implant (CI) recipients. ECochG signals reflect the state of the inner ear and can be measured during implantation and post-operatively. The aim of our study was to apply an objective deep learning (DL)-based algorithm to assess the reproducibility of longitudinally recorded ECochG signals, compare them with audiometric hearing thresholds, and identify signal patterns and tonotopic behavior.

Methods

We used a previously published objective DL-based algorithm to evaluate post-operative intracochlear ECochG signals collected from 21 ears. The same measurement protocol was repeated three times over 3 months. Additionally, we measured the pure-tone thresholds and subjective loudness estimates for correlation with the objectively detected ECochG signals. Recordings were made on at least four electrodes at three intensity levels. We extracted the electrode positions from computed tomography (CT) scans and used this information to evaluate the tonotopic characteristics of the ECochG responses.

Results

The objectively detected ECochG signals exhibited substantial repeatability over a 3-month period (bias-adjusted kappa, 0.68; accuracy 83.8%). Additionally, we observed a moderate-to-strong dependence of the ECochG thresholds on audiometric and subjective hearing levels. Using radiographically determined tonotopic measurement positions, we observed a tendency for tonotopic allocation with a large variance. Furthermore, maximum ECochG amplitudes exhibited a substantial basal shift. Regarding maximal amplitude patterns, most subjects exhibited a flat pattern with amplitudes evenly distributed over the electrode carrier. At higher stimulation frequencies, we observed a shift in the maximum amplitudes toward the basal turn of the cochlea.

Conclusions

We successfully implemented an objective DL-based algorithm for evaluating post-operative intracochlear ECochG recordings. We can only evaluate and compare ECochG recordings systematically and independently from experts with an objective analysis. Our results help to identify signal patterns and create a better understanding of the inner ear function with the electrode in place. In the next step, the algorithm can be applied to intra-operative measurements.

An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning

Electrocochleography (ECochG) measures electrophysiological inner ear potentials in response to acoustic stimulation. These potentials reflect the state of the inner ear and provide important information about its residual function. For cochlear implant (CI) recipients, we can measure ECochG signals directly within the cochlea using the implant electrode. We are able to perform these recordings during and at any point after implantation. However, the analysis and interpretation of ECochG signals are not trivial. To assist the scientific community, we provide our intracochlear ECochG data set, which consists of 4,924 signals recorded from 46 ears with a cochlear implant. We collected data either immediately after electrode insertion or postoperatively in subjects with residual acoustic hearing. This data descriptor aims to provide the research community access to our comprehensive electrophysiological data set and algorithms. It includes all steps from raw data acquisition to signal processing and objective analysis using Deep Learning. In addition, we collected subject demographic data, hearing thresholds, subjective loudness levels, impedance telemetry, radiographic findings, and classification of ECochG signals.

Influence of Electric Frequency-to-Place Mismatches on the Early Speech Recognition Outcomes for Electric-Acoustic Stimulation Users

PURPOSE

Cochlear implant (CI) recipients with hearing preservation experience significant improvements in speech recognition with electric-acoustic stimulation (EAS) as compared to with a CI alone, although outcomes across EAS users vary. The individual differences in performance may be due in part to default mapping procedures, which result in electric frequency-to-place mismatches for the majority of EAS users. This study assessed the influence of electric mismatches on the early speech recognition for EAS users.

METHOD

Twenty-one participants were randomized at EAS activation to listen exclusively with a default or place-based map. For both groups, the unaided thresholds determined the acoustic cutoff frequency (i.e., > 65 dB HL). For default maps, the electric filter frequencies were assigned to avoid spectral gaps in frequency information but created varying magnitudes of mismatches. For place-based maps, the electric filter frequencies were assigned to avoid frequency-to-place mismatches. Recognition of consonant-nucleus-consonant words and vowels was assessed at activation and 1, 3, and 6 months postactivation.

RESULTS

For participants with default maps, electric mismatch at 1500 Hz ranged from 2 to -12.0 semitones (Mdn = -5 semitones). Poorer performance was observed for those with larger magnitudes of electric mismatch. This effect was observed through 6 months of EAS listening experience.

CONCLUSIONS

The present sample of EAS users experienced better initial performance when electric mismatches were small or eliminated. These data suggest the utility of methods that reduce electric mismatches, such as place-based mapping procedures. Investigation is ongoing to determine whether these differences persist with long-term EAS use.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.22096523.

A Computational Model of a Single Auditory Nerve Fiber for Electric-Acoustic Stimulation

Cochlear implant (CI) recipients with preserved acoustic low-frequency hearing in the implanted ear are a growing group among traditional CI users who benefit from hybrid electric-acoustic stimulation (EAS). However, combined ipsilateral electric and acoustic stimulation also introduces interactions between the two modalities that can affect the performance of EAS users. A computational model of a single auditory nerve fiber that is excited by EAS was developed to study the interaction between electric and acoustic stimulation. Two existing models of sole electric or acoustic stimulation were coupled to simulate responses to combined EAS. Different methods of combining both models were implemented. In the coupled model variant, the refractoriness of the simulated fiber leads to suppressive interaction between electrically evoked and acoustically evoked spikes as well as spontaneous activity. The second model variant is an uncoupled EAS model without electric-acoustic interaction. By comparing predictions between the coupled and the noninteracting EAS model, it was possible to infer electric-acoustic interaction at the level of the auditory nerve. The EAS model was used to simulate fiber populations with realistic inter-unit variability, where each unit was represented by the single-fiber model. Predicted thresholds and dynamic ranges, spike rates, latencies, jitter, and vector strengths were compared to empirical data. The presented EAS model provides a framework for future studies of peripheral electric-acoustic interaction.

Objectification of intracochlear electrocochleography using machine learning

Introduction

Electrocochleography (ECochG) measures inner ear potentials in response to acoustic stimulation. In patients with cochlear implant (CI), the technique is increasingly used to monitor residual inner ear function. So far, when analyzing ECochG potentials, the visual assessment has been the gold standard. However, visual assessment requires a high level of experience to interpret the signals. Furthermore, expert-dependent assessment leads to inconsistency and a lack of reproducibility. The aim of this study was to automate and objectify the analysis of cochlear microphonic (CM) signals in ECochG recordings.

Methods

Prospective cohort study including 41 implanted ears with residual hearing. We measured ECochG potentials at four different electrodes and only at stable electrode positions (after full insertion or postoperatively). When stimulating acoustically, depending on the individual residual hearing, we used three different intensity levels of pure tones (i.e., supra-, near-, and sub-threshold stimulation; 250–2,000 Hz). Our aim was to obtain ECochG potentials with differing SNRs. To objectify the detection of CM signals, we compared three different methods: correlation analysis, Hotelling's T² test, and deep learning. We benchmarked these methods against the visual analysis of three ECochG experts.

Results

For the visual analysis of ECochG recordings, the Fleiss' kappa value demonstrated a substantial to almost perfect agreement among the three examiners. We used the labels as ground truth to train our objectification methods. Thereby, the deep learning algorithm performed best (area under curve = 0.97, accuracy = 0.92), closely followed by Hotelling's T² test. The correlation method slightly underperformed due to its susceptibility to noise interference.

Conclusions

Objectification of ECochG signals is possible with the presented methods. Deep learning and Hotelling's T² methods achieved excellent discrimination performance. Objective automatic analysis of CM signals enables standardized, fast, accurate, and examiner-independent evaluation of ECochG measurements.

Forward Electric Stimulation-Induced Interference in Intracochlear Electrocochleography of Acoustic Stimulation in the Cochlea of Guinea Pigs

Electric-acoustic stimulation (EAS) uses amplified sound by a hearing aid to stimulate an apical low-frequency region of the cochlea and electrical current from a cochlear implant (CI) to stimulate the basal high-frequency region. EAS recipients had significantly improved speech perception, music appreciation, and hearing function in noise compared to those relying on CI electrical stimulation (ES) alone. However, the interaction between basal ES and apical acoustic stimulation (AS) in the cochlea potentially affects EAS advantages. To investigate ES-AS interaction, we designed a system that recorded the electrically evoked compound action potential (ECAP) and the auditory evoked potential (AEP). We used an intracochlear electrode array to deliver ES at the basal cochlea and detect intracochlear electrocochleography (iECochG) generated from apical AS. Within iECochG, 3 or 6 dB (double or quadruple intensity of ECAP threshold) electric stimulation, 1 ms-forward ES significantly increased CAP amplitudes of 4 kHz/20 dB AS compared to 0 dB ES. Notably, 1 ms-forward 3 dB ES significantly increased CAP amplitudes of 4 kHz/20 dB AS, while 3 or 5 ms-forward ES did not change the CAP amplitudes. The elevation in CAP amplitude of 40 dB/4 kHz AS induced by 1 ms-forward 3 dB ES was significantly lower than that in 20 dB/4 kHz AS. With 1 ms-forward 3 dB ES, AS frequency and stimulating electrode location have no significant impact on relative CAP amplitudes of 20 dB AS. These results suggest that the basal forward ES and the following apical AS could produce a cumulative effect on the auditory nerve response.

Phantom Stimulation for Cochlear Implant Users With Residual Low-Frequency Hearing

In cochlear implants (CIs), phantom stimulation can be used to extend the pitch range toward apical regions of the cochlea. Phantom stimulation consists of partial bipolar stimulation, in which current is distributed across two intracochlear electrodes and one extracochlear electrode as defined by the compensation coefficient σ. The aim of this study was, (1) to evaluate the benefit of conveying low-frequency information through phantom stimulation for cochlear implant (CI) subjects with low-frequency residual hearing using electric stimulation alone, (2) to compare the speech reception thresholds obtained from electric-acoustic stimulation (EAS) and electric stimulation in combination with phantom stimulation (EPS), and (3) to investigate the effect of spectrally overlapped bandwidth of speech conveyed via simultaneous acoustic and phantom stimulation on speech reception thresholds.

Establishing Reproducibility and Correlation of Cochlear Microphonic Amplitude to Implant Electrode Position Using Intraoperative Electrocochleography and Postoperative Cone Beam Computed Tomography

Supplemental Digital Content is available in the text.

Objectives:

The primary objective of this study was to establish the reproducibility of cochlear microphonic (CM) recordings obtained from a cochlear implant (CI) electrode contact during and immediately after insertion. This was achieved by evaluating the insertion angle and calculating the position of the apical electrode contact during insertion, using postoperative cone beam computed tomography (CBCT). The secondary objective was to create individualized patient maps of electrode contacts located within acoustically sensitive regions by correlating the CM amplitude to the electrode position determined using CBCT.

Methods:

CMs were recorded from a CI electrode contact during and immediately after insertion in 12 patients (n = 14 ears). Intraoperative recordings were made for a 0.5 kHz tone burst stimulus and were recorded from the apical electrode contact. Postinsertion recordings were made from the odd-numbered electrode contacts (1–15) along the array, using a range of stimulus frequencies (from 0.125 to 2 kHz). The time point at which each electrode contact passed through the round window was noted throughout the insertion, and the CM amplitude at this point was correlated to postoperative CBCT. This correlation was then used to estimate the CM amplitude at particular points within the cochlea, which was in turn compared with the amplitudes recorded from each electrode postoperatively to assess the reproducibility of the recordings.

Results:

Significant correlation was shown between intraoperative insertion and postinsertion angles at two amplitude events (maximum amplitude: 29° mean absolute error, r = 0.77, p = 0.006; 10% of maximum amplitude: 52° mean absolute error, r = 0.85, p = 0.002).

Conclusion:

We have developed a novel method to demonstrate the reproducibility of the CM responses recorded from a CI electrode during insertion. By correlating the CM amplitude with the postoperative CBCT, we have also been able to create individualized maps of CM responses, categorizing the cochlea into acoustically responsive and unresponsive regions. If the electrode contacts within the acoustically sensitive regions are shown to be associated with improved loudness discrimination, it could have implications for optimal electrode mapping and placement.

The role of electroneural versus electrophonic stimulation on psychoacoustic electric-acoustic masking in cochlear implant users with residual hearing

Cochlear implant (CI) candidates with residual low-frequency hearing are nowadays often implanted with CI electrode arrays that allow preserving their acoustic hearing in the implanted ear. These subjects receiving combined electric-acoustic stimulation (EAS) show enhanced speech perception scores when compared to traditional CI users without acoustic component. However, these benefits are limited by interaction effects such as masking between electric and acoustic stimulation. This study evaluates ipsilateral electric-acoustic masking in a psychophysical experiment conducted in 5 EAS subjects. The elevation of acoustic pure tone thresholds through simultaneous presentation of electric pulse trains and vice versa is measured for different acoustic frequencies and different settings of the electric stimuli. Electric-acoustic interaction could originate either from electroneural stimulation of auditory nerve fibers or from electrophonic stimulation of hair cells. The two fundamental goals of this study are to investigate the effects of stimulation rate and phase duration of the electric stimulus on electric-acoustic masking and to investigate the origin of electric-acoustic masking by assessing the contributions of electroneural versus electrophonic stimulation. The amount of electric-acoustic masking in the present study was independent of pulse rate and phase duration of the electric stimuli. Moreover, the results demonstrate that electric-acoustic masking depends on the spatial distance between the locations of electric or acoustic excitation in the cochlea, but not on the spectral content of the electric stimulus. We thereby conclude that psychoacoustic electric-acoustic masking in EAS users is dominated by electroneural-acoustic interaction, whereas the contribution of electrophonic stimulation is negligible.