Efficient Epileptic Seizure Prediction Based on Deep Learning

• Published in: IEEE transactions on biomedical circuits and systems Vol. 13; no. 5; pp. 804 - 813
• Main Authors: Daoud, Hisham, Bayoumi, Magdy A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adolescent; Algorithms; Artificial neural networks; Brain modeling; Child; Child, Preschool; Classification; Classification algorithms; Convulsions & seizures; Deep Learning; EEG; Electroencephalography; Epilepsy; False alarms; Feature extraction; Female; Humans; interictal; Machine learning; Male; Models, Neurological; Neural networks; Neurological diseases; Optimization; Prediction algorithms; preictal; Real time; Recurrent neural networks; seizure prediction; Seizures; Seizures - physiopathology; Signal processing; Signal Processing, Computer-Assisted; Test procedures; Transfer learning
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2019.2929053

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 computations. Four deep learning models are proposed to extract the most discriminative features which enhance the classification accuracy and prediction time. The proposed approach takes advantage of the convolutional neural network in extracting the significant spatial features from different scalp positions and the recurrent neural network in expecting the incidence of seizures earlier than the current methods. A semi-supervised approach based on transfer learning technique is introduced to improve the optimization problem. A channel selection algorithm is proposed to select the most relevant EEG channels which makes the proposed system good candidate for real-time usage. An effective test method is utilized to ensure robustness. The achieved highest accuracy of 99.6% and lowest false alarm rate of 0.004 h - 1 along with very early seizure prediction time of 1 h make the proposed method the most efficient among the state of the art.