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Automatic classification of seizure and seizure-free EEG signals based on phase space reconstruction features

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Abstract

Epilepsy is a type of brain disorder triggered by an abrupt electrical imbalance of neuronal networks. An electroencephalogram (EEG) is a diagnostic tool to capture the underlying brain mechanisms and detect seizure onset in epileptic patients. To detect seizures, neurologists need to manually monitor EEG recordings for long periods, which is challenging and susceptible to errors depending on expertise and experience. Therefore, automatic identification of seizure and seizure-free EEG signals becomes essential. This study introduces a method based on the features extracted from the phase space reconstruction for classifying seizure and seizure-free EEG signals. The computed features are derived from the elliptical area and interquartile range of the Euclidean distance by varying percentage values of data points ranging from 50 to 100%. We consider two public datasets and evaluate these features in each EEG epoch that includes the healthy, interictal, preictal, and ictal stages of epileptic subjects, utilizing the K-nearest neighbor classifier for classification. Results show that the features have higher values during the seizure than the seizure-free EEG signals and healthy subjects. Furthermore, the proposed features can effectively discriminate seizure EEG signals from the seizure-free and normal subjects with 100% accuracy, sensitivity, and specificity in both datasets. Likewise, the classification between the preictal stage and seizure EEG signals attains 98% accuracy. Overall, the reconstructed phase space features significantly enhance the accuracy of detecting epileptic EEG signals compared with existing methods. This advancement holds great potential in assisting neurologists in swiftly and accurately diagnosing epileptic seizures from EEG signals.

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Material and/or code availability

The EEG data for this study was obtained from an open-source EEG database from the University of Bonn and Neurology and Sleep Centre of Hauz Khas, New Delhi.

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Acknowledgements

The author Shervin Skaria wishes to acknowledge Mahatma Gandhi University, Kerala, India, for providing fellowship for the research program. The authors also wish to thank the Neurology and Sleep Centre of Hauz Khas, New Delhi, and the University of Bonn for providing the EEG data for the present study.

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Authors and Affiliations

  1. Department of Physics, Government College Kottayam, Nattakom, Kerala, India

    Shervin Skaria & Sreelatha Karyaveetil Savithriamma

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Contributions

The authors confirm their contribution to the paper as follows: Shervin Skaria conducts the preliminary investigation, data analysis, methodology, programming, interprets the results, prepares the figures, and writes the original manuscript. Sreelatha K.S supervised, reviewed, and edited the manuscript. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Sreelatha Karyaveetil Savithriamma.

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Skaria, S., Savithriamma, S.K. Automatic classification of seizure and seizure-free EEG signals based on phase space reconstruction features. J Biol Phys (2024). https://doi.org/10.1007/s10867-024-09654-6

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