Electrocochleography in Cochlear Implant Recipients: Correlating Maximum Response With Residual Hearing.

OBJECTIVES: Electrocochleography (ECochG) is increasingly recognized as a biomarker for assessing inner ear function in cochlear implant patients. This study aimed to objectively determine intraoperative cochlear microphonic (CM) amplitude patterns and correlate them with residual hearing in cochlear implant recipients, addressing the limitations in current ECochG analysis that often depends on subjective visual assessment and overlook the intracochlear measurement location. DESIGN: In this prospective study, we investigated intraoperative pure-tone ECochG following complete electrode insertion in 31 patients. We used our previously published objective analysis method to determine the maximum CM amplitude and the associated electrode position for each electrode array. Using computed tomography, we identified electrode placement and determined the corresponding tonotopic frequency using Greenwood's function. Based on this, we calculated the tonotopic shift, that is, the difference between the stimulation frequency and the estimated frequency of the electrode with the maximum CM amplitude. We evaluated the association between CM amplitude, tonotopic shift, and preoperative hearing thresholds using linear regression analysis. RESULTS: CM amplitudes showed high variance, with values ranging from -1.479 to 4.495 dBµV. We found a statistically significant negative correlation ( ) between maximum CM amplitudes and preoperative hearing thresholds. In addition, a significant association ( ) between the tonotopic shift and preoperative hearing thresholds was observed. Tonotopic shifts of the maximum CM amplitudes occurred predominantly toward the basal direction. CONCLUSIONS: The combination of objective signal analysis and the consideration of intracochlear measurement locations enhances the understanding of cochlear health and overcomes the obstacles of current ECochG analysis. We could show the link between intraoperative CM amplitudes, their spatial distributions, and preoperative hearing thresholds. Consequently, our findings enable automated analysis and bear the potential to enhance specificity of ECochG, reinforcing its role as an objective biomarker for cochlear health.

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.