gms | German Medical Science

73. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
Joint Meeting mit der Griechischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

29.05. - 01.06.2022, Köln

Developping an epileptic seizure warning system

Entwicklung eines Warnsystems für epileptische Anfälle

Meeting Abstract

  • presenting/speaker Georg Leonhardt - Carl Gustav Carus Universitätsklinikum, TU Dresden, Klinik und Poliklinik für Neurochirurgie, Dresden, Deutschland
  • Matthias Eberlein - Technische Universität Dresden, Professur Grundlagen Elektrotechnik, Dresden, Deutschland
  • Hongliu Yang - Technische Universität Dresden, Professur Grundlagen Elektrotechnik, Dresden, Deutschland
  • Jens Müller - Technische Universität Dresden, Professur Grundlagen Elektrotechnik, Dresden, Deutschland
  • Sotirios Kalousios - Carl Gustav Carus Universitätsklinikum, TU Dresden, Klinik und Poliklinik für Neurochirurgie, Dresden, Deutschland
  • Matthias Kirsch - Carl Gustav Carus Universitätsklinikum, TU Dresden, Klinik und Poliklinik für Neurochirurgie, Dresden, Deutschland
  • Ronald Tetzlaff - Technische Universität Dresden, Professur Grundlagen Elektrotechnik, Dresden, Deutschland
  • Gabriele Schackert - Carl Gustav Carus Universitätsklinikum, TU Dresden, Klinik und Poliklinik für Neurochirurgie, Dresden, Deutschland

Deutsche Gesellschaft für Neurochirurgie. 73. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), Joint Meeting mit der Griechischen Gesellschaft für Neurochirurgie. Köln, 29.05.-01.06.2022. Düsseldorf: German Medical Science GMS Publishing House; 2022. DocP203

doi: 10.3205/22dgnc519, urn:nbn:de:0183-22dgnc5198

Veröffentlicht: 25. Mai 2022

© 2022 Leonhardt et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Objective: Background: One third of epilepsy patients do not become seizure free from medical therapy and only few benefit from surgery or brain stimulation. They suffer from the unpredictability of seizures, falls, social stigmatisation etc, and would profit by a seizure warning device, offering opportunities of protection and intervention.

Aim: to develop an implantable mobile warning system, we analysed algorithms for binary classification of intracranial EEG data (iEEG) to identify preictal states.

Methods: iEEG from 14 patients with longstanding pharmacorefractory epilepsy (n=13 depth electrodes, n=1 subdural electrodes, seizures recorded 3 – 38, duration of recording 139-257 h per patient) were analysed. Seizure were annotated by an experienced epileptologist (GL). A preictal episode was defined as the time-period from 65 to 5 min before a seizure and an interictal episode was defined beginning 240 min after a seizure and before the next seizure, respectivelly.

Patient specific models were estimated based on the patient’s previous seizures using discrete spectral density bands, inter-channel correlation in the time and frequency domain, and eigenvalues of the correlation matrices; followed by analysis of variance (ANCOVA f-value) and finally classification by a support vector machine (SVM).

Main outcome parameter was the identification of a true preictal episode (=prediction) of the last seizure of the patient. The previous seizures were used to train the system

Results: The last seizure could be predicted correctly in 10 out of 14 pts (i.e. ROC AUC (SVM) > 0.5, Table 1 [Tab. 1]) with a mean of 0.71 over all pts. Further tests showed that a prediction based only on the principal component (PC) was possible for 8 of 14 pts. (ROC AUC (PC), mean 0.683).

Conclusion: Our data shows that seizures can be predicted in the majority of patients but that the algorithm is sensitive to other dynamics such (PC) such as the sleep-wake-cycle. These confounding factors contribute to the unstationarity of the ground truth. This has negative effects on the training in rather short-term data sets (Müller et al. [1], Eberlein et al. [2]).


References

1.
Müller J, Yang H, Eberlein M, Leonhardt G, Uckermann O, Kuhlmann L, Tetzlaff R. Coherent false seizure prediction in epilepsy, coincidence or providence? Clin Neurophysiol. 2022 Jan;133:157-164. DOI: 10.1016/j.clinph.2021.09.022 Externer Link
2.
Eberlein M, et al. Evaluation of machine learning methods for seizure prediction in epilepsy. Curr Dir in Biomed Eng. 2019;5(1):109-112.