ArticleScientific data2023
An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Prediction of Auditory Performance in Cochlear Implants Using Machine Learning Methods: A Systematic Review.Audiology research · 2025Review
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
- Identifying Slim Modiolar Electrode Tip Fold-Over With Intracochlear Electrocochleography.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2024Article
- Computational methods in glaucoma research: Current status and future outlook.Molecular aspects of medicine · 2023Review
- An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning.Scientific data · 2023Article
- Objective evaluation of intracochlear electrocochleography: repeatability, thresholds, and tonotopic patterns.Frontiers in neurology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.