An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning
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...
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| Published in | Scientific data Vol. 10; no. 1; pp. 157 - 11 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
22.03.2023
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2052-4463 2052-4463 |
| DOI | 10.1038/s41597-023-02055-9 |
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| 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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| AbstractList | 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. 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.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. 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. |
| ArticleNumber | 157 |
| Author | Gawliczek, Tom Weder, Stefan Caversaccio, Marco Dalbert, Adrian Rummel, Christian Wimmer, Wilhelm Schuerch, Klaus Mantokoudis, Georgios |
| Author_xml | – sequence: 1 givenname: Klaus orcidid: 0000-0002-4352-7174 surname: Schuerch fullname: Schuerch, Klaus organization: Department of ENT, Head and Neck Surgery, Inselspital, Bern University Hospital, University of Bern, Hearing Research Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern – sequence: 2 givenname: Wilhelm surname: Wimmer fullname: Wimmer, Wilhelm organization: Department of ENT, Head and Neck Surgery, Inselspital, Bern University Hospital, University of Bern, Hearing Research Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern – sequence: 3 givenname: Adrian surname: Dalbert fullname: Dalbert, Adrian organization: Department of Otorhinolaryngology, Head&Neck Surgery, University Hospital Zurich, University of Zurich – sequence: 4 givenname: Christian surname: Rummel fullname: Rummel, Christian organization: Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern – sequence: 5 givenname: Marco orcidid: 0000-0002-7090-8087 surname: Caversaccio fullname: Caversaccio, Marco organization: Department of ENT, Head and Neck Surgery, Inselspital, Bern University Hospital, University of Bern, Hearing Research Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern – sequence: 6 givenname: Georgios surname: Mantokoudis fullname: Mantokoudis, Georgios organization: Department of ENT, Head and Neck Surgery, Inselspital, Bern University Hospital, University of Bern – sequence: 7 givenname: Tom orcidid: 0000-0002-9480-7133 surname: Gawliczek fullname: Gawliczek, Tom organization: Department of ENT, Head and Neck Surgery, Inselspital, Bern University Hospital, University of Bern – sequence: 8 givenname: Stefan surname: Weder fullname: Weder, Stefan email: stefan.weder@insel.ch organization: Department of ENT, Head and Neck Surgery, Inselspital, Bern University Hospital, University of Bern, Hearing Research Laboratory, ARTORG Center for Biomedical Engineering Research, University of Bern |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36949075$$D View this record in MEDLINE/PubMed |
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| Title | An intracochlear electrocochleography dataset - from raw data to objective analysis using deep learning |
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