Research Area: Machine Learning
Intracranial electroencephalogram (iEEG) recorded at cerebral cortex contains a lot of important information for the diagnosis of epilepsy. Currently, the diagnosis of epilepsy must be performed by multiple clinical experts through visual judgment on the long term interictal iEEG signals. However, it is a time consuming and extremely difficult process. In this paper, we introduce the feature extraction method based on the several different entropies evaluated on the different frequency bands, which can thus be formed as a 2D feature map. Then, we employ the convolutional neural network (CNN) to train a binary classifier based on the labels provided by clinical experts. The experimental results on public benchmark and real-world iEEG recorded from patients demonstrate that our method can achieve 99.0% classification performance. Hence, it is a promising technique to reduce the workload of clinical experts for automatic detection of epileptic focal.
Keywords:
Detection Of Epileptic
Intracranial electroencephalogram (iEEG)
Convolutional Neural Network
Machine Learning
Deep Learning
Author(s) Name: Xuyang Zhao; Qibin Zhao; Toshihisa Tanaka; Jianting Cao; Wanzeng Kong; Hidenori Sugano; Noboru Yoshida
Journal name:
Conferrence name: IEEE 23rd International Conference on Digital Signal Processing
Publisher name: IEEE
DOI: 10.1109/ICDSP.2018.8631885
Volume Information:
