Identification of pre-ictal states based on an EEG-ECG multi-feature clustering approach
A seizure prediction system can improve lives of patients with epilepsy. Although seizure prediction has been a widely-researched topic, there is still no reliable and robust system that has strong theoretical support to be able to differentiate between inter-ictal and pre-ictal states. This study a...
- Autores:
-
Flórez Torres, Nicolás
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/44053
- Acceso en línea:
- http://hdl.handle.net/1992/44053
- Palabra clave:
- Procesamiento de señales - Técnicas digitales - Investigaciones
Epilepsia
Electroencefalografía - Investigaciones
Electrocardiografía - Investigaciones
Aprendizaje automático (Inteligencia artificial)
Ingeniería
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
| Summary: | A seizure prediction system can improve lives of patients with epilepsy. Although seizure prediction has been a widely-researched topic, there is still no reliable and robust system that has strong theoretical support to be able to differentiate between inter-ictal and pre-ictal states. This study aims at identify if there is a natural grouping of data that allows differentiating these pre- seizure states using different strategies of electroencephalography (EEG) and electrocardiographic (ECG) signal characteristics of epileptic patients and allowing the anticipation of seizures using algorithms of unsupervised machine learning, seeking to be a reliable tool for predicting seizures. |
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