Publication2017
Pediatric Seizure prediction from EEG signals based on unsupervised learning techniques using various distance measures
The record
- Type
- Publication
- Date
- 19 October 2017
- Level
- National
- Journal
- 2017 1st International Conference on Electronics Materials Engineering and Nano Technology Iementech 2017
- Published
- 19 October 2017
- DOI
- 10.1109/iementech.2017.8076983
- Scopus ID
- 85039937115
- Authors
- 4
- Open access
- No
- Citations
- 9
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3 Ensure healthy lives and promote well-being for all at all ages
Unsupervised k-means clustering for seizure prediction from EEG signals achieves 91.43% accuracy in pediatric patients enabling early epilepsy detection
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Machine learning methodology for neurological disease detection from biomedical signals advancing research innovation
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
Pediatric seizure prediction technology supporting early diagnosis and improved management of childhood epilepsy
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Pediatric Seizure prediction from EEG signals based on unsupervised learning techniques using various distance measures”. Kortex.