Automated Diagnosis of Epilepsy Using Key-Point-Based Local Binary Pattern of EEG Signals

The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filte...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2017-07, Vol.21 (4), p.888-896
Main Authors: Tiwari, Ashwani Kumar, Pachori, Ram Bilas, Kanhangad, Vivek, Panigrahi, Bijaya Ketan
Format: Article
Language:English
Subjects:
Artificial neural networks
Automation
Classification
Computation
Computer-assisted diagnosis
Databases, Factual
Diagnosis
Diagnosis, Computer-Assisted - methods
EEG
Electroencephalography
Electroencephalography - methods
Epilepsy
Epilepsy - diagnosis
Feature extraction
Histograms
Humans
Informatics
local binary pattern (LBP)
Medical diagnosis
Methodology
Seizing
Seizures
Signal Processing, Computer-Assisted
Sons
Support Vector Machine
support vector machine (SVM) classifier
Support vector machines
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Summary:The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy. Our method involves detection of key points at multiple scales in EEG signals using a pyramid of difference of Gaussian filtered signals. Local binary patterns (LBPs) are computed at these key points and the histogram of these patterns are considered as the feature set, which is fed to the support vector machine (SVM) for the classification of EEG signals. The proposed methodology has been investigated for the four well-known classification problems namely, 1) normal and epileptic seizure, 2) epileptic seizure and seizure free, 3) normal, epileptic seizure, and seizure free, and 4) epileptic seizure and nonseizure EEG signals using publically available university of Bonn EEG database. Our experimental results in terms of classification accuracies have been compared with existing methods for the classification of the aforementioned problems. Further, performance evaluation on another EEG dataset shows that our approach is effective for classification of seizure and seizure-free EEG signals. The proposed methodology based on the LBP computed at key points is simple and easy to implement for real-time epileptic seizure detection.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2016.2589971