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Updated: Aug 6, 2025
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Performing Intracochlear Electrocochleography During Cochlear Implantation
Published on: March 8, 2022
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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
Summary
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.
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.
