| Autor: |
Christian Riccio, Roberta Siciliano, Michele Staiano, Giuseppe Longo, Luigi Pavone, Gaetano Zazzaro |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Neuroscience Informatics, Vol 4, Iss 3, Pp 100168- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2772-5286 |
| DOI: |
10.1016/j.neuri.2024.100168 |
| Popis: |
Epilepsy is a severe and common neurological disease that causes sudden and irregular seizures, necessitating patient-specific detection models for effective management. The proposed methodology, Epilepsy Tracking META-Set Analysis, establishes portability rules that identify similar patients, enabling the transfer of these detection models from one patient to another. Main issue is to identify clusters of patients analyzing a set of meta-features of each patient in terms of clinical descriptors, performance metrics of a machine learning model for seizure detection, and data complexity measures. The investigation of complexity measures represents a novelty in such a medical field, allowing to compare patients and to support automated seizure detection methods. The proposed methodology is validated using the well-known Epileptic Seizure EEG Database from the Epilepsy Center of the University Hospital of Freiburg and demonstrates promising results in transferring detection models to new cases. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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