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Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings
- Source :
- iScience, Vol 24, Iss 1, Pp 101997-(2021), iScience
- Publication Year :
- 2021
- Publisher :
- Elsevier, 2021.
-
Abstract
- Summary 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.<br />Graphical Abstract<br />Highlights • 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<br />Computer Application in Medicine; Computer-Aided Diagnosis Method; Clinical Neuroscience; Techniques in Neuroscience; Algorithms
- Subjects :
- 0301 basic medicine
Computer science
Brain activity and meditation
02 engineering and technology
Article
03 medical and health sciences
ALARM
Techniques in Neuroscience
medicine
Leverage (statistics)
Ictal
Time domain
lcsh:Science
Multidisciplinary
business.industry
Clinical Neuroscience
Pattern recognition
021001 nanoscience & nanotechnology
Computer Application in Medicine
Computer-Aided Diagnosis Method
030104 developmental biology
medicine.anatomical_structure
Binary classification
Scalp
lcsh:Q
Artificial intelligence
Data pre-processing
0210 nano-technology
business
Algorithms
Subjects
Details
- Language :
- English
- ISSN :
- 25890042
- Volume :
- 24
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- iScience
- Accession number :
- edsair.doi.dedup.....431181f8682f9977f2e70d0d6bcab1cd