A deep learning based ensemble learning method for epileptic seizure prediction

人工智能 计算机科学 癫痫 卷积神经网络 深度学习 癫痫发作 集成学习 机器学习 支持向量机 脑电图 模式识别(心理学) 分类器(UML) 特征(语言学) 心理学 神经科学 哲学 语言学
作者
Syed Muhammad Usman,Shehzad Khalid,Sadaf Bashir
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:136: 104710-104710 被引量:108
标识
DOI:10.1016/j.compbiomed.2021.104710
摘要

In epilepsy, patients suffer from seizures which cannot be controlled with medicines or surgical treatments in more than 30% of the cases. Prediction of epileptic seizures is extremely important so that they can be controlled with medication before they actually occur. Researchers have proposed multiple machine/deep learning based methods to predict epileptic seizures; however, accurate prediction of epileptic seizures with low false positive rate is still a challenge. In this research, we propose a deep learning based ensemble learning method to predict epileptic seizures. In the proposed method, EEG signals are preprocessed using empirical mode decomposition followed by bandpass filtering for noise removal. The class imbalance problem has been mitigated with synthetic preictal segments generated using generative adversarial networks. A three-layer customized convolutional neural network has been proposed to extract automated features from preprocessed EEG signals and combined them with handcrafted features to get a comprehensive feature set. The feature set is then used to train an ensemble classifier that combines the output of SVM, CNN and LSTM using Model agnostic meta learning. An average sensitivity of 96.28% and specificity of 95.65% with an average anticipation time of 33 min on all subjects of CHBMIT has been achieved by the proposed method, whereas, on American epilepsy society-Kaggle seizure prediction dataset, an average sensitivity of 94.2% and specificity of 95.8% has been achieved on all subjects.
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