Detection of focal epilepsy in brain maps through a novel pattern recognition technique
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Focal epilepsy is a common neurological disorder of the brain. It is symbolized by recurring seizure activities in particular regions of the brain. Electroencephalogram (EEG) signals contain information about the brain activity that can be used to identify areas affected by seizures. For this, however, neurologists have the challenging task to analyze the EEG signal to imply where the source of epilepsy is located. A novel approach to detect regions of focal epilepsy is proposed in this paper. By using an adaptive mixture of independent component analysis as a decomposition algorithm and information about the position of the measuring electrodes, EEG signals are transformed into two-dimensional brain maps that are further altered by a symmetric-weighted scale-invariant local ternary pattern technique. Features from this altered brain map are used as input in a three-layered artificial neural network for classification between epileptic and artifact brain maps. The brain maps classified as artifact are eliminated, and the epileptic components are given as output for doctors to investigate the regions of the brain affected by focal epilepsy. With the proposed methodology, an accuracy of 99.53% to detect epileptic components in EEG signals is reached.
KeywordsElectroencephalography Classification Artificial neural network Symmetric-weighted scale-invariant local ternary pattern (SWSILTP) technique
This study was funded by the Department of Science and Technology (TSDP), Ministry of Science and Technology, Government of India [Grant Numbers DST/TSG/ICT/2015/54-G, 2015].
Compliance with ethical standards
Conflict of interest
Corresponding author has received research grants from Department of Science and Technology (TSDP), Ministry of Science and Technology, Government of India.
- 1.Andrzejak RG, Lehnertz K, Mormann F et al (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Top 64:061907. https://doi.org/10.1103/PhysRevE.64.061907 CrossRefGoogle Scholar
- 7.Meier R, Dittrich H, Schulze-Bonhage A, Aertsen A (2008) Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns. J Clin Neurophysiol 25:119–131. https://doi.org/10.1097/WNP.0b013e3181775993 CrossRefGoogle Scholar
- 25.Belouchrani A, Abed-Meraim K, Cardoso JF, Moulines E (1993) Second order blind separation of temporally correlated sources. In: Proceedings of the international conference on digital signal processing, pp 346–351Google Scholar
- 28.Leutheuser H, Gabsteiger F, Hebenstreit F et al (2013) Comparison of the AMICA and the InfoMax algorithm for the reduction of electromyogenic artifacts in EEG data. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS, pp 6804–6807Google Scholar
- 29.Palmer J, Kreutz-Delgado K, Makeig S (2011) AMICA: an adaptive mixture of independent component analyzers with shared components. Technical report, Swart Centre for Computational Neuroscience, San Diego, CAGoogle Scholar
- 33.Liao S, Zhao G, Kellokumpu V et al (2010) Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 1301–1306Google Scholar
- 43.Tan W, Li B, Zhang W (2010) Research on background modeling method based on center-symmetric local binary patterns. J Univ Sci Technol China 11:004Google Scholar
- 44.Zhang Z, Xiao B, Wang C et al (2011) Background modeling by exploring multi-scale fusion of texture and intensity in complex scenes. In: The first Asian conference on pattern recognition. IEEE, pp 402–406Google Scholar
- 46.Puri M, Solanki A, Padawer T et al (2016) Introduction to artificial neural network (ANN) as a predictive tool for drug design, discovery, delivery, and disposition. Artif Neural Netw Drug Des Deliv Dispos. https://doi.org/10.1016/B978-0-12-801559-9.00001-6 CrossRefGoogle Scholar
- 47.Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. In: Anderson JA, Rosenfeld E (eds) Neurocomputing: foundations of research. MIT Press, Cambridge, pp 696–699Google Scholar
- 55.Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings—2005 IEEE computer society conference on computer vision and pattern recognition, CVPR 2005, pp 886–893Google Scholar
- 57.Behnam M, Pourghassem H (2016) Real-time seizure prediction using RLS filtering and interpolated histogram feature based on hybrid optimization algorithm of Bayesian classifier and Hunting search. Comput Methods Programs Biomed 132:115–136. https://doi.org/10.1016/j.cmpb.2016.04.014 CrossRefGoogle Scholar