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Diagnosis of epileptic EEG using a lagged Poincare plot in combination with the autocorrelation

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Abstract

In this paper, an efficient simple system for classifying electroencephalogram (EEG) data of normal and epileptic subjects is presented using lagged Poincare plot parameters. To this effect, a benchmark for choosing delays is defined based on the autocorrelation function. For each lag, traditional indicators, including the number of points lying on the identity line, the length of the minor (SD1)/major axis (SD2) of the fitted ellipse on the plot, the SD1/SD2 ratio, and the area of the ellipse, were calculated. The efficiency of the features in discriminating between the groups was examined based on the statistical significance of the differences. K-nearest neighbor and probabilistic neural network were employed as the classifier. The performance of the suggested scheme was evaluated using a publicly available database that includes numerous EEG data of healthy, during the incidence of an epileptic seizure and seizure-free intervals cases. It is indicated that the method can provide the maximum correct rate of 98.33%. Our results indicated the proposed scheme could characterize the dynamics of EEG signals in three groups, and it is suitable for the detection of epileptic seizures.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran

    Atefeh Goshvarpour

  2. Department of Biomedical Engineering, Imam Reza International University, Rezvan Campus (Female Students), Phalestine Sq., P.O. Box 91735-553, Mashhad, Razavi Khorasan, Iran

    Ateke Goshvarpour

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Goshvarpour, A., Goshvarpour, A. Diagnosis of epileptic EEG using a lagged Poincare plot in combination with the autocorrelation. SIViP 14, 1309–1317 (2020). https://doi.org/10.1007/s11760-020-01672-w

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