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Related Experiment Video

Updated: Aug 6, 2025

Performing Intracochlear Electrocochleography During Cochlear Implantation
09:10

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

Klaus Schuerch1,2, Wilhelm Wimmer1,2, Adrian Dalbert3

  • 1Department of ENT, Head and Neck Surgery, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Scientific Data
|March 23, 2023
PubMed
Summary
This summary is machine-generated.

This study shares a large dataset of electrocochleography (ECochG) signals from cochlear implant patients. The data and algorithms aim to improve the analysis of these crucial inner ear function measurements.

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Area of Science:

  • Audiology
  • Neuroscience
  • Biomedical Engineering

Background:

  • Electrocochleography (ECochG) measures inner ear potentials, reflecting residual cochlear function.
  • Intracochlear ECochG in cochlear implant (CI) recipients offers direct electrophysiological insights.
  • Analyzing ECochG signals presents significant challenges for researchers.

Purpose of the Study:

  • To provide a comprehensive dataset of intracochlear ECochG signals for the scientific community.
  • To share algorithms for raw data acquisition, signal processing, and Deep Learning analysis.
  • To facilitate objective analysis and interpretation of ECochG data in CI users.

Main Methods:

  • Collected 4,924 intracochlear ECochG signals from 46 ears with cochlear implants.
  • Recorded data during and after electrode insertion, including subjects with residual hearing.
  • Developed and applied Deep Learning algorithms for objective signal analysis.

Main Results:

  • A large, well-characterized dataset of intracochlear ECochG signals is now publicly available.
  • The dataset includes associated demographic, audiological, and clinical data.
  • Algorithms for signal processing and Deep Learning-based analysis are provided.

Conclusions:

  • This dataset and associated tools will advance research in cochlear implant audiology.
  • Improved ECochG analysis can lead to better understanding of CI function and patient outcomes.
  • Facilitating data sharing promotes reproducibility and innovation in the field.