Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 945
- Type
- D - Journal article
- DOI
-
10.1155/2015/986736
- Title of journal
- BIOMED RESEARCH INTERNATIONAL
- Article number
- ARTN 986736
- First page
- -
- Volume
- 2015
- Issue
- -
- ISSN
- 2314-6133
- Open access status
- Out of scope for open access requirements
- Month of publication
- January
- Year of publication
- 2015
- 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
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 45
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is an output of a collaboration between LJMU and neurologists from The Walton Centre NHS Foundation Trust to investigate a generalised seizure detection system using machine learning. It has led to a current collaboration with St. George’s University Hospitals NHS Foundation Trust (Dr. Khaled Abdel-Aziz, consultant neurologist, sharon.frank@stgeorged.nhs.uk) to look at pre-ictal data, serialised as Gramian Angular and Markov Transition Fields and image features using stacked autoencoders to classify the early onset of seizures. Generalised seizure detection is the first study of its kind.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
