Paper Info
Title
Epileptic Seizure Detection And Anticipation Using Deep Learning With Ordered Encoding Of Spectrogram Features
Abstract
Electroencephalogram (EEG) signals of the brain play a vital role in the detection of epileptic seizures. This paper proposes a new spectrogram-based deep learning method for the detection and anticipation of epileptic seizures. Unlike the existing methods, the proposed method formulates the feature descriptor such that it retains the neighborhood order of spectrograms both in time and frequency, while significantly reducing the dimensionality of the feature descriptor. The spectrogram in each of the 18 EEG channels is constructed by dividing each EEG signal into 3 time-blocks and 19 frequency-blocks. The mean magnitude value of each of these blocks is computed and thereby compactly representing the input EEG signal by a 3D tensor of size 18 x 19 x 3. This tensor descriptor is given as an input to the proposed convolution neural network for learning high-level features. Evaluations are performed on a publicly available EEG dataset of 23 patients and the results from the proposed method are compared with 9 other existing methods. Further, a five-class classification is performed using the proposed method for the anticipation of seizures. The proposed method is found to outperform the existing state-of-the-art methods both in detection and anticipation of epileptic seizures.
Year
DOI
Venue
2020
10.23919/Eusipco47968.2020.9287559
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020)
Keywords
DocType
ISSN
Epileptic Seizures, Seizure Detection, Seizure Anticipation, Electroencephalogram (EEG) Signal, Multi-channel EEG, Spectrogram, Deep Learning, Convolutional Neural Network (CNN)
Conference
2076-1465
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Sameer Ranjan Sahu100.34
Rama Krishna Sai Subrahmanyam Gorthi222.05
Subrahmanyam Gorthi300.34