Detection_of_spontaneous_seizures_in_EEGs_in_multiple_experimental_mouse_models_of_epilepsy.pdf (1.76 MB)
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Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy

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journal contribution
posted on 08.06.2022, 08:12 by Lan Wei, Halima Boutouil, Rogério GerbatinRogério Gerbatin, Omar MamadOmar Mamad, Mona HeilandMona Heiland, Cristina Ruedell ReschkeCristina Ruedell Reschke, Federico Del Gallo, Paolo F Fabene, David HenshallDavid Henshall, Madeleine Lowery, Gareth Morris, Catherine Mooney
Objective. Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals. Approach. In this study, a machine learning-based seizure detection approach, 'Epi-AI', which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach. Main results. Epi-AI demonstrated a superior sensitivity, 91.4%-98.8%, and specificity, 93.1%-98.8%, in Models I-III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I-III ranged from 66.9%-91.3% sensitivity and 60.8%-97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I-III at 73.2%-80.1% and 75.8%-98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity. Significance. Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.


Science Foundation Ireland (SFI) under Grant Number 16/RC/3948

European Regional Development Fund

European Union Seventh Framework (FP7) ‘EpimiRNA’ project under grant agreement 602130

Marie Skłodowska-Curie Actions Individual Fellowship ‘EpimiRTherapy’, H2020-MSCA-IF-2018 840262

FutureNeuro industry partners



The original article is available at

Published Citation

Wei L. et al. Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy. J Neural Eng. 2021;18(5):056060

Publication Date

19 October 2021

PubMed ID



  • FutureNeuro Centre
  • Physiology and Medical Physics
  • School of Pharmacy and Biomolecular Sciences

Research Area

  • Neurological and Psychiatric Disorders


IOP Publishing


  • Published Version (Version of Record)