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ConceFT-based epileptic seizure detection via transfer learning

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Abstract

The classical Wavelet Transform (WT) is still useful and highly popular analysis method, especially for non-stationary signals like biomedical ones. Unfortunately, performance of the WT is very much depending on the chosen mother wavelet. ConceFT (concentration of frequency and time) is a brand-new time–frequency (TF) analysis method which combines multitaper technique (MT) and synchrosqueezing transform (SST). This combination produces highly concentrated TF representations with approximately perfect time and frequency resolutions. Also, ConceFT undermines the importance of the mother wavelet. ConceFT approach produces same TF representations for different mother wavelets owing to combination of MT technique and SST. In this paper, we aim to show the TF representation performance and robustness of ConceFT by using it for the solution of the epileptic seizure detection problem. We propose a seizure detection algorithm which uses ConceFT as a TF method and transfer learning. Epilepsy is a common neurological disorder that millions of people suffer worldwide. Daily life of the patients is very difficult because of the unpredictable time of seizures. Electroencephalography signals that monitor the electrical activity of the brain can be used to detect approaching seizures and make possible to warn the patient before the attack. For evaluating the classification performance of our ConceFT-based approach, we prefer to use GoogLeNet and SqueezeNet which are the well-known deep learning models. We tested our method for various classification scenarios and obtained accuracies between 91.18 and 100% for two- and three-class classification scenarios. High results show that ConceFT is a successful and promising TF analysis method for non-stationary signals.

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The EEG signals used in this work can be obtained from the University of Bonn [24].

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Acknowledgements

This work was supported by Scientific Research Projects Coordination Unit of Istanbul University—Cerrahpasa, with project number FBA-2021-36160. Mosab A. A. Yousif would like to thank to the Turkish Presidency for Turks Abroad and the University of Gezira in Sudan for their funding of this project.

Funding

This work was supported by Scientific Research Projects Coordination Unit of Istanbul University—Cerrahpasa, with project number 36160. Ph.D. studentship of Mosab A. A. Yousif has been supported by the Turkish Presidency for Turks Abroad and the University of Gezira in Sudan.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Institute of Graduate Studies, Istanbul University - Cerrahpasa, Istanbul, Türkiye

    Mosab A. A. Yousif

  2. Department of Electrical and Electronics Engineering, Engineering Faculty, Istanbul University - Cerrahpasa, Istanbul, Türkiye

    Mahmut Ozturk

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  1. Mosab A. A. Yousif
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  2. Mahmut Ozturk
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Contributions

MAAY carried out the simulations, analyzed the results, and wrote the paper; MO configured the method, analyzed the results, and wrote the paper.

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Correspondence to Mahmut Ozturk.

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The authors declare that they have no conflict of interest.

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The experiments and data collection were approved by the local ethics committee as mentioned in [24]. The authors of this work have accepted the ethics rules and obeyed them during the research.

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The EEG signals of patients with epilepsy are recorded at University of Bonn, and they are publicly available for scientific research [24].

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Yousif, M.A.A., Ozturk, M. ConceFT-based epileptic seizure detection via transfer learning. SIViP (2024). https://doi.org/10.1007/s11760-024-03077-5

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