Simultaneous Intra- and Extracochlear Electrocochleography During Cochlear Implantation to Enhance Response Interpretation

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.

Classification of Acoustic Hearing Preservation After Cochlear Implantation Using Electrocochleography

The objective to preserve residual hearing during cochlear implantation has recently led to the use of intracochlear electrocochleography (ECochG) as an intraoperative monitoring tool. Currently, a decrease in the amplitude of the difference between responses to alternating-polarity stimuli (DIF response), predominantly reflecting the hair cell response, is used for providing feedback. Including other ECochG response components, such as phase changes and harmonic distortions, could improve the accuracy of surgical feedback. The objectives of the present study were (1) to compare simultaneously recorded stepwise intracochlear and extracochlear ECochG responses to 500 Hz tone bursts, (2) to explore patterns in features extracted from the intracochlear ECochG recordings relating to hearing preservation or hearing loss, and (3) to design support vector machine (SVM) and random forest (RF) classifiers of acoustic hearing preservation that treat each subject as a sample and use all intracochlear ECochG recordings made during electrode array insertion for classification. Forty subjects undergoing cochlear implant (CI) surgery at the Oslo University Hospital, St. Thomas' Hearing Implant Centre, or the University Hospital of Zurich were prospectively enrolled. In this cohort, DIF response amplitude decreases did not relate to postoperative acoustic hearing preservation. Exploratory analysis of the feature set extracted from the ECochG responses and preoperative audiogram showed that the features were not discriminative between outcome classes. The SVM and RF classifiers that were trained on these features could not distinguish cases with hearing loss and hearing preservation. These findings suggest that hearing loss following CI surgery is not always reflected in intraoperative ECochG recordings.

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.

Automatic analysis of cochlear response using electrocochleography signals during cochlear implant surgery

Cochlear implants (CIs) provide an opportunity for the hearing impaired to perceive sound through electrical stimulation of the hearing (cochlear) nerve. However, there is a high risk of losing a patient’s natural hearing during CI surgery, which has been shown to reduce speech perception in noisy environments as well as music appreciation. This is a major barrier to the adoption of CIs by the hearing impaired. Electrocochleography (ECochG) has been used to detect intra-operative trauma that may lead to loss of natural hearing. There is early evidence that ECochG can enable early intervention to save natural hearing of the patient. However, detection of trauma by observing changes in the ECochG response is typically carried out by a human expert. Here, we discuss a method of automating the analysis of cochlear responses during CI surgery. We establish, using historical patient data, that the proposed method is highly accurate (∼94% and ∼95% for sensitivity and specificity respectively) when compared to a human expert. The automation of real-time cochlear response analysis is expected to improve the scalability of ECochG and improve patient safety.