Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings

Manuel Ruiz Marín, Irene Villegas Martínez, Germán Rodríguez Bermúdez, Maurizio Porfiri

Research output: Contribution to journalArticlepeer-review

Abstract

Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.

Original languageEnglish (US)
Article number101997
JournaliScience
Volume24
Issue number1
DOIs
StatePublished - Jan 22 2021

Keywords

  • Algorithms
  • Clinical Neuroscience
  • Computer Application in Medicine
  • Computer-Aided Diagnosis Method
  • Techniques in Neuroscience

ASJC Scopus subject areas

  • General

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