Schuerch, Klaus; Wimmer, Wilhelm; Dalbert, Adrian; Rummel, Christian; Caversaccio, Marco; Mantokoudis, Georgios; Gawliczek, Tom; Weder, Stefan (2023). An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning. Scientific data, 10(1), p. 157. Nature Publishing Group 10.1038/s41597-023-02055-9
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s41597-023-02055-9.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (1MB) | Preview |
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.
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