Diagnosis of epileptic EEG using a lagged Poincare plot in combination with the autocorrelation
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ORIGINAL PAPER
Diagnosis of epileptic EEG using a lagged Poincare plot in combination with the autocorrelation Atefeh Goshvarpour1 · Ateke Goshvarpour2 Received: 27 April 2019 / Revised: 14 January 2020 / Accepted: 9 March 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
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. Keywords Lagged Poincare plot · Lag selection · Autocorrelation · Electroencephalography · Epilepsy detection
1 Introduction The noninvasive technique for monitoring electrical activity of the brain is called “electroencephalogram (EEG).” Today, the evaluation of brain signals is an integral part of the diagnosis, detection, classification, prediction, or even treatment of brain diseases/disorders [1–6]. In addition, in appraising psychological problems and emotions, these signals have been used frequently [7–10]. The visual inspection of EEG is a laborious and timeconsuming process and entails the assistance of a well-trained expert. Therefore, many attempts have been made to develop an automatic algorithm. Usually, a typical EEG monitoring
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Ateke Goshvarpour [email protected]; [email protected] Atefeh Goshvarpour [email protected]
1
Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
2
Department of Biomedical Engineering, Imam Reza International University, Rezvan Campus (Female Students), Phalestine Sq., P.O. Box 91735-553, Mashhad, Razavi Khorasan, Iran
system integrates a number of modules. More or less, they are as follows: signal recording, data pre-processing, feature extraction, optimal feature selection, and classification. Depending on the application, the role of some modules becomes more pronounced. However, particular attention is paid to the design of the feature extraction unit. Often, the speed and precision of computing
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