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Normal routine electroencephalograms (EEGs) can cause delays in the diagnosis and treatment of epilepsy, especially in drug-resistant patients and those without structural abnormalities. There is a need for alternative quantitative approaches that can inform clinical decisions when traditional visual EEG review is inconclusive. We leverage a large population EEG database (N = 13,652 recordings, 12,134 unique patients) and an independent cohort of patients with focal epilepsy (N = 121) to investigate whether normal EEG segments could support the diagnosis of focal epilepsy. We decomposed expertly graded normal EEGs (N = 6,242) using unsupervised tensor decomposition to extract the dominant spatio-spectral patterns present in a clinical population. We then, using the independent cohort of patients with focal epilepsy, evaluated whether pattern loadings of normal interictal EEG segments could classify focal epilepsy, the epileptogenic lobe, presence of lesions, and drug response. We obtained six physiological patterns of EEG spectral power and connectivity with distinct spatio-spectral signatures. Both pattern types together effectively differentiated patients with focal epilepsy from non-epileptic controls (mean AUC 0.78) but failed to classify the epileptogenic lobe. Spectral power-based patterns best classified drug-resistant epilepsy (mean AUC 0.73) and lesional epilepsy (mean AUC 0.67), albeit with high variability across patients. Our findings support that visibly normal patient EEGs contain subtle quantitative differences of clinical relevance. Further development may yield normal EEG-based computational biomarkers that can augment traditional EEG review and epilepsy care.
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Composing graphical models with generative adversarial networks for EEG signal modeling
Neural oscillations in the form of electroencephalogram (EEG) can reveal underlying brain functions, such as cognition, memory, perception, and consciousness. A comprehensive EEG computational model provides not only a stochastic procedure that directly generates data but also insights to further understand the neurological mechanisms. Here, we propose a generative and inference approach that combines the complementary benefits of probabilistic graphical models and generative adversarial networks (GANs) for EEG signal modeling. We investigate the method’s ability to jointly learn coherent generation and inverse inference models on the CHI-MIT epilepsy multi-channel EEG dataset. We further study the efficacy of the learned representations in epilepsy seizure detection formulated as an unsupervised learning problem. Quantitative and qualitative experimental results demonstrate the effectiveness and efficiency of our approach.
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- PAR ID:
- 10417516
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
- Format(s):
- Medium: X
- Location:
- Singapore
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/ICASSP43922.2022.9747783
