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Epilepsy is a brain disorder that causes seizures, affecting nearly half a million children in the US alone. In this study, we aimed to use a nonlinear driven method to characterize scalp EEG recordings of pediatric epilepsy patients (PE: n=7) compared to pediatric control subjects (PC: n=7) in a clinical environment. A time-varying approach was used to construct functional connectivity networks (FCNs) of all subjects. Next, the FCNs are mapped into the form of undirected graphs that are subjected to the extraction of graph theory-based features. An unsupervised clustering technique based on K-mean is used to delineate the PE from the PC group. Our findings show a statistically significant difference in the mean FCNs between PC and PE groups (t(340)=- 15.9899, p<< 0.0001). Performance results showed an accuracy of 92.5% with a sensitivity of 90% and a specificity of 95.3%. This approach can help improve and validate the early diagnosis of PE by applying non-invasive scalp EEG signals.
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An Integrated Approach for Focal Cortical Dysplasia Lesion Validation on Preoperative Assessments
Pediatric epilepsy due to drug-resistant Focal Cortical Dysplasia (FCD) presents significant healthcare challenges. Precise preoperative identification of FCD lesions is imperative for surgical planning and patient outcomes. This paper presents a proof-of-concept for an integrated methodology that combines Electroencephalogram (EEG)-based functional connectivity analysis with Magnetic Resonance Imaging (MRI)-derived cortical thickness measurements to identify FCD lesions in pediatric epileptic patients. We examined a single-case clinical scenario from Oregon Health Science and University, consistently identifying the Caudal Middle Frontal (cMFG) region across both EEG and MRI modalities, a finding that was confirmed in the postoperative MRI scan. This cross-validation underscores the potential of the precision of our approach in pinpointing the surgical target region. Despite being constrained by its preliminary nature, our research offers a valuable foundation for a personalized, rigorous method of detecting the location of the FCD lesions. It holds significant clinical implications for managing FCD-related epilepsy. It also portends broader applications in neurology and precision medicine. Nonetheless, further large-scale studies are needed to validate and fine-tune our methodology. Clinical Relevance - This study offers clinicians an advanced, integrated approach to preoperative assessment of FCD lesions, potentially improving the precision of surgical planning in pediatric epilepsy.
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- PAR ID:
- 10541406
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-1050-4
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Location:
- Pittsburgh, PA, USA
- Sponsoring Org:
- National Science Foundation
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Epilepsy is a brain disorder that causes seizures, affecting nearly half a million children in the US alone. In this study, we aimed to use a nonlinear driven method to characterize scalp EEG recordings of pediatric epilepsy patients (PE: n=7 ) compared to pediatric control subjects (PC: n=7 ) in a clinical environment. A time-varying approach was used to construct functional connectivity networks (FCNs) of all subjects. Next, the FCNs are mapped into the form of undirected graphs that are subjected to the extraction of graph theory-based features. An unsupervised clustering technique based on K-mean is used to delineate the PE from the PC group. Our findings show a statistically significant difference in the mean FCNs between PC and PE groups (t(340)=−15.9899,p<<0.0001) . Performance results showed an accuracy of 92.5% with a sensitivity of 90% and a specificity of 95.3%. This approach can help improve and validate the early diagnosis of PE by applying non-invasive scalp EEG signals.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/BHI58575.2023.10313373
