Ruiz Marín M, Villegas Martínez I, Rodríguez Bermúdez G, Porfiri M. Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings.
iScience 2021;
24:101997. [PMID:
33490905 PMCID:
PMC7811137 DOI:
10.1016/j.isci.2020.101997]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 10/23/2020] [Accepted: 12/23/2020] [Indexed: 11/23/2022] Open
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.
Complexity measures are formulated to enhance classical time-domain statistics of EEG
The detection algorithm does not need ad-hoc data preprocessing to address artifacts
Focal seizures are detected 95% of the time with less than four false alarms per day
The approach offers a visual representation of a seizure as a time-evolving network
Collapse