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Automatic and manual prediction of epileptic seizures based on ECG

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Abstract

This study presents a new attempt to quantify and predict changes in the ECG signal in the pre-ictal period. In the proposed approach, threshold techniques were applied to the standard deviation of two heart rate variability features (The number of heartbeats per two minutes and approximate entropy) computed to ensure prediction and quantification of the pre-ictal state. We analyzed clinical data taken from two epileptic public databases, Siena scalp EEG and post-ictal heart rate oscillations in partial epilepsy and a local database. By testing the proposed approach on the Siena scalp EEG database, we achieved a sensitivity of 100%, specificity of 95%, and an accuracy of 96.4% whereas using acquisitions from the post-ictal database, we achieved a sensitivity of 100%, specificity of 91% and an accuracy of 94% and using the local database we achieved a sensitivity of 100%, a specificity of 97% and an accuracy of 97.5%. Furthermore, the proposed approach predicted 58.7%, 57.2, and 40% of the seizures before the onset by more than 10 min for the data taken from post-ictal, local and Siena database, respectively. Using the automatic threshold technique, we were able to achieve a sensitivity, specificity, and accuracy of 85%, 81%, 82% using our local database, respectively, whereas using acquisitions take from the Siena scalp EEG database, we achieved a sensitivity of 75%, specificity of 85% and an accuracy of 82%. Besides, using the post-ictal database, we achieved a sensitivity of 90%, a specificity of 83% and an accuracy of 85%.

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Data availability

The datasets Siena scalp EEG and post-ictal Heart Rate Oscillations in partial epilepsy analyzed during the current study are available in the physionet platform [15, 27].The local database analyzed during the current study is available in the functional brain tractography project [28].

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Authors and Affiliations

  1. Laboratoire de Technologie et Imagerie Médicale, Facultéde Médecine de Monastir, Universitéde Monastir, 5019, Monastir, Tunisia

    Manef Ben Mbarek, Ines Assali, Salah Hamdi, Asma Ben Abdallah & Mohamed Hedi Bedoui

  2. Faculté des Sciences de Monastir, Université de Monastir, 90 Ave Mohamed V, 1002, Monastir, Tunisia

    Manef Ben Mbarek & Ines Assali

  3. Institut Supérieur d’Informatique et de Mathématiques, Université de Monastir, 5019, Monastir, Tunisia

    Asma Ben Abdallah

  4. Faculté de Médecine, Institut de Neurosciences des Systèmes, Inserm UMR1106, Aix-Marseille Université, 27, Boulevard Jean Moulin, 13005, Marseille, France

    Olivier David & Marcel Carrere

  5. Hopital Universitaire Fattouma Bourguiba, 5019, Monastir, Tunisia

    Mouna Aissi

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  1. Manef Ben Mbarek
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Contributions

MBM, ABA, and MC conceptualized the proposed idea. MBM, IA, SH, ABA, MC, MHB checked the analytical methods. MBM, OD and MA did the data curation. MBM and MC, IA analyzed the data. MBM and MC carried out the computations. MBM, IA, SH, ABA, MC and MHB contributed to the evaluation of the results. MBM, ABA, MC and MHB wrote the manuscript in cooperation with the other authors. All authors discussed the results, contributed to the final manuscript and have approved of the final article.

Corresponding author

Correspondence to Manef Ben Mbarek.

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Conflict of interest

This work was financially supported by the “PHC-Utique” program of the French Ministry of Foreign Affairs and Ministry of Higher Education and Research and the Tunisian Ministry of Higher Education and Scientific Research in the CMCU project number 21G1402.

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Ben Mbarek, M., Assali, I., Hamdi, S. et al. Automatic and manual prediction of epileptic seizures based on ECG. SIViP (2024). https://doi.org/10.1007/s11760-024-03063-x

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