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Robust dimensionality-reduced epilepsy detection system using EEG wavelet packets and machine learning

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Abstract

Purpose

In response to the challenges in epileptic seizure detection arising from the high dimensionality of EEG signals and the localization of the epileptogenic focus, this paper presents a robust EEG-type-independent system designed to tackle these issues by means of wavelet packet features (WPF) and both supervised and unsupervised machine learning algorithms. Moreover, our study underscores the significance of mother wavelet selection to enhance accuracy and effectiveness in epilepsy detection.

Methods

The developed system consists of two complementary subsystems, namely the Representative EEG Channel Creator (RECC) and the Seizure Affected EEG Channel Detector (SAECD). The RECC utilizes WPF to generate representative channels from EEG signals, subsequently feeding them into an LDA classifier for epilepsy detection. If the RECC returns a positive result, the SAECD is then launched to track the seizure’s diffusion path using the Energy-to-Shannon-Entropy ratio and k-means clustering.

Results

Using db6 mother wavelet, the RECC achieved a high sensitivity of 98.46% for epileptic seizure identification with a significant reduction in EEG dimensionality with up to 93.75%. On the other hand, through the developed SAECD, we were able to circumscribe the epileptogenic area with an average silhouette range of [51.21\(-\)88.18]%.

Conclusion

The outcomes of the developed system are promising, confirming that epileptic seizures may indeed be detected within a few amount of data, providing evidence in terms of processing time and efficiency.

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Data Availibility

The EEG data used in this study are publicly available from two repositories: the MIT-CHB database (https://physionet.org/content/chbmit/) and the Bonn database (https://www.ukbonn.de/epileptologie/arbeitsgruppen/ag-lehnertz-neurophysik/downloads/).

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Acknowledgements

The authors thank the Directorate General of Scientific Research and Technological Development (Direction Générale de la Recherche Scientifique et du Développement Technologique, DGRSDT, URL: www.dgrsdt.dz, Algeria) for their financial support.

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  1. Laboratory of Biomedical Engineering, Faculty of Technology, University of Tlemcen, BP 230, Chetouane, Tlemcen, 13000, Algeria

    Lyna Henaa Hasnaoui & Abdelghani Djebbari

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  1. Lyna Henaa Hasnaoui
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  2. Abdelghani Djebbari
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Correspondence to Abdelghani Djebbari.

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Hasnaoui, L.H., Djebbari, A. Robust dimensionality-reduced epilepsy detection system using EEG wavelet packets and machine learning. Res. Biomed. Eng. (2024). https://doi.org/10.1007/s42600-024-00355-6

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