Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach

Yauhen Statsenko, Vladimir Babushkin, Tatsiana Talako, Tetiana Kurbatova, Darya Smetanina, Gillian Lylian Simiyu, Tetiana Habuza, Fatima Ismail, Taleb M. Almansoori, Klaus N.V. Gorkom, Miklós Szólics, Ali Hassan, Milos Ljubisavljevic

Research output: Contribution to journalArticlepeer-review

Abstract

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95–100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Original languageEnglish
Article number2370
JournalBiomedicines
Volume11
Issue number9
DOIs
Publication statusPublished - Sept 2023

Keywords

  • EEG
  • acquisition settings
  • activation maximization
  • deep learning
  • epileptic seizure
  • interpretable machine learning
  • source reconstruction

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • General Biochemistry,Genetics and Molecular Biology

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