Epileptic seizure classification using the NeuroMov database
Abstract
Epilepsy is one of the most common neurological disorders, affecting up to 1% of the world population. In cases where epileptics are not responsive to medication, resective surgery has been found to be an effective treatment for seizure freedom or increase in control. For this, epilepsy must be correctly diagnosed. That evaluation is typically performed by VideoEEG monitoring by physicians, a subjective task that can easily be aided by movement quantification and pattern recognition techniques. Regarding their onset location, seizures can be classified as having extratemporal or temporal origin. This contribution contains an analysis of infrared data from 143 seizures from 31 different patients, recorded in the Epilepsy Monitoring Unit of University of Munich, for seizure classification. Regarding classification, using the seizures' duration and the existence of movements of interest, an 86% ± 17% AUC is obtained using 10-fold cross validation and a Support Vector Machine model. Using the video recordings, the region of interest (bed) was first detected with 88% correct detections and 22% overdetections using a threshold-based method, and all beds were rotated to a vertical position for consistency. Lastly, seizures were classified with a Convolutional Neural Network and a Multilayer Perceptron, obtaining a 65% AUC and showing that the model is better at classifying extratemporal seizures, mostly due to class imbalance. The developed approach shows potential for clinical decision support using a non-intrusive and low cost solution.
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Maia et al. [157] achieved 65% AUC for classifying epileptic seizures.
“Maia et al. [157] applied a threshold-based approach to first detect regions of interest (beds) in video data, aligning them vertically for consistency, then utilized Convolutional Neural Networks and Multilayer Perceptrons to classify epileptic seizures, achieving 65% AUC.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [157]
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- Authors
- Maia, P., Hartl, E., Vollmar, C., Noachtar, S., & Silva Cunha, J. P.
- Affiliations
- INESC TEC (PT)
- Ludwig-Maximilians-Universität München (DE)
- Venue
- 2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)
- Publisher
- IEEE
- Published
- 2019-02
- Pages
- 1-4
- OpenAlex
- W2938634896
- Cited by
- 14
- References
- 12
- Citations by year
- 2026: 1 · 2024: 4 · 2022: 1 · 2021: 5 · 2020: 2 · 2019: 1
- FWCI
- 3.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Portuguese Meeting on Bioengineering (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Health Sciences › Medicine › Psychiatry and Mental health › Epilepsy research and treatment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- epileptic seizure classification, video-EEG monitoring, movement quantification, temporal lobe epilepsy, epilepsy surgery, convolutional neural networks, support vector machine, class imbalance, clinical decision support
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