Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection

作者：

Highlights：

• Epileptic seizure detection models were developed.

• The effectiveness of stacking ensemble approach was examined.

• All models were designed in Python by utilizing ‘Keras’ library with Tensorflow.

• Stacking ensemble-based model with 97.17% accuracy outperformed the baseline model.

摘要

•Epileptic seizure detection models were developed.•The effectiveness of stacking ensemble approach was examined.•All models were designed in Python by utilizing ‘Keras’ library with Tensorflow.•Stacking ensemble-based model with 97.17% accuracy outperformed the baseline model.

论文关键词：Epileptic seizure,Electroencephalography signals,Stacking approach,Deep neural networks,K-fold cross-validation,Performance improvement

论文评审过程：Received 6 September 2019, Revised 22 January 2020, Accepted 23 January 2020, Available online 23 January 2020, Version of Record 31 January 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113239