Efficient Epileptic Seizure Prediction Based on Deep Learning
University of Louisiana at Lafayette
Indexed incrossrefpubmed
Abstract
Epilepsy is one of the world's most common neurological diseases. Early prediction of the incoming seizures has a great influence on epileptic patients' life. In this paper, a novel patient-specific seizure prediction technique based on deep learning and applied to long-term scalp electroencephalogram (EEG) recordings is proposed. The goal is to accurately detect the preictal brain state and differentiate it from the prevailing interictal state as early as possible and make it suitable for real time. The features extraction and classification processes are combined into a single automated system. Raw EEG signal without any preprocessing is considered as the input to the system which further reduces the…
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Authors
2Topics & keywords
Topics
Keywords
- Computer science
- Artificial intelligence
- Discriminative model
- Electroencephalography
- Ictal
- Deep learning
- Pattern recognition (psychology)
- Convolutional neural network
UN Sustainable Development Goals
- Reduced inequalities
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