Research Area: Machine Learning
Monitoring and recording brain activities using Electroencephalograms (EEGs) has become the foremost wide applied tool by physicians for epilepsy diagnosis due to viable reasons like its availability, simplicity, and low cost. In this paper, we propose an automatic epileptic seizure detection framework based on deep learning techniques that are applied to raw EEG signals recordings without the overhead of features extraction. The proposed framework uses one-dimensional deep convolutional autoencoder for features extraction and dimensionality reduction. Three different neural networks systems classifiers are evaluated. Classification between normal and ictal cases has achieved 100% accuracy on all systems. The best classification results between the normal, interictal and ictal cases accomplished a 99.33% average overall accuracy using Bidirectional Long Short-Term Memory.
Keywords:
Epileptic Seizure Detection
Deep Convolutional Autoencoder
Classification
neural networks
Long Short-Term Memory
Machine Learning
Deep Learning
Author(s) Name: Ahmed M. Abdelhameed; Hisham G. Daoud; Magdy Bayoumi
Journal name:
Conferrence name: IEEE International Workshop on Signal Processing Systems (SiPS)
Publisher name: IEEE
DOI: 10.1109/SiPS.2018.8598447
Volume Information:
