Detection of Interictal Discharges With Convolutional Neural Networks Using Discrete Ordered Multichannel Intracranial EEG
- Submitting institution
-
Royal Holloway and Bedford New College
- Unit of assessment
- 12 - Engineering
- Output identifier
- 31369213
- Type
- D - Journal article
- DOI
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10.1109/TNSRE.2017.2755770
- Title of journal
- IEEE Transactions on Neural Systems and Rehabilitation
- Article number
- -
- First page
- 2285
- Volume
- 25
- Issue
- 12
- ISSN
- 1534-4320
- Open access status
- Out of scope for open access requirements
- Month of publication
- September
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
6
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Our preliminary results on “Deep learning for epileptic intracranial EEG data” attracted considerable attention from scientists and clinicians (e.g. Army Research Lab and University Medical Centre Freiburg). This polarised attention was due to our qualitative approach rather than quantitative results. As such, the visualisation of our qualitative results facilitates the *clinical* interpretation of seizure data as shown by Alice D Lam from Massachusetts General Hospital. We designed a comprehensive set of additional experiments to ensure that our qualitative results are not by “mere chance” and were well received at our IEEE tutorial on “From brains to deep neural networks” (https://wcci2020.org/tutorials-ijcnn/).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
