Convolutional Neural Networks for Early Seizure Alert System
| Autor: | T. Iešmantas, R. Alzbutas |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Deep learning 05 social sciences Pattern recognition Electroencephalography medicine.disease Convolutional neural network 050105 experimental psychology 03 medical and health sciences Epilepsy ComputingMethodologies_PATTERNRECOGNITION 0302 clinical medicine Seizure detection medicine 0501 psychology and cognitive sciences Artificial intelligence Transfer of learning business Alert system Classifier (UML) 030217 neurology & neurosurgery |
| Zdroj: | Precision Medicine Powered by pHealth and Connected Health ISBN: 9789811074189 |
| DOI: | 10.1007/978-981-10-7419-6_4 |
| Popis: | A general framework of a system for early seizure detection and alert is presented. Many studies have shown high potential of electroencephalograms (EEG) when there are used together with machine learning algorithms for seizure/non-seizure classification task. In this paper, mainly guidelines will be presented on how to use convolutional neural networks for the purpose of highly accurate classification of non-invasive EEG for patients with epilepsy. Convolutional neural networks can be pre-trained on a sample data as described in this paper and then implemented into an application or a device, which readjusts its parameters according to the patient-specific EEG patterns and thus can be further used as a seizure monitoring and alert system. The paper also demonstrated how transfer learning can be applied to create a patient-specific classifier with high accuracy. |
| Databáze: | OpenAIRE |
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