Classification of interictal epileptiform discharges using partial directed coherence Conference

cited authors

  • Janwattanapong, P; Cabrerizo, M; Fang, C; Rajaei, H; Pinzon-Ardila, A; Gonzalez-Arias, S; Adjouadi, M

abstract

  • This paper introduces the classification of patterns extracted from different types of interictal epileptiform discharges (IEDs) that includes interictal spike (IS), spike and slow wave complex (SSC), and repetitive spikes and slow wave complex (RSS)), using the partial directed coherence (PDC) analysis. The PDC analysis estimates the intensity and direction of propagation from neural activities generated in the cerebral cortex, and analyzes the coefficients obtained from employing multivariate autoregressive model (MVAR). Features extracted by using PDC are transformed into binary matrices by using surrogate data testing with a 0.05 significance level. The significant propagations are represented as 1 in the binary matrix and 0 otherwise. Binary matrices are converted into binary vectors. These vectors are then selected as the inputs of a multilayer Perceptron (MLP) neural network. The first classifier is trained to distinguish between 2 types of IEDs and tenfold cross validation is implemented to evaluate the system. The performance of the classifier was evaluated, where it achieved the highest F1 score of 100.00% when performed on IS vs RSS and 96.67% on IS vs CSS. The average F1 score of the first classifier obtained was 91.11%. The second classifier was trained to perform all types of IEDs classifications. The classifier yielded an overall accuracy of 86.67% with the highest achieved F1 score of 90.00%. Both classifiers were able to detect and classify different types of IEDs when using the features extracted from PDC with a very high performance.

publication date

  • July 1, 2017

Digital Object Identifier (DOI)

start page

  • 473

end page

  • 478

volume

  • 2018-January