AI Article Synopsis
- * A study involving 24 epilepsy patients used various statistical methods to identify these preictal states from clinical scores, yielding promising results with a detection accuracy (AUC) of 0.71 and a positive predictive value (PPV) of 0.77.
- * These findings suggest that integrating such detection algorithms into mobile devices could enhance the monitoring and prediction of seizures, and including additional clinical data might improve sensitivity to subtle physiological changes.
Article Abstract
Previous findings have suggested that a preictal state might precede the epileptic seizure onset, which is the basis for seizure prediction attempts. Preictal states can be apprehended as outliers that differ from an interictal baseline and display clinical changes. We collected daily clinical scores from patients with epilepsy who underwent continuous video-EEG and assessed the ability of several outlier detection methods to identify preictal states. Results from 24 patients suggested that outlying clinical features were suggestive of preictal states and can be identified by statistical methods: AUC = 0.71, 95 % CI = [0.63 - 0.79]; PPV = 0.77, 95 % CI = [0.70 - 0.84]; FPR = 0.31, 95 % CI = [0.21 - 0.44]); and F1 score = 0.74, 95 % CI = [0.64 - 0.81]. Such algorithms could be straightforwardly implemented in a mobile device (e.g., tablet or smartphone), which would allow a longer data collection that could improve prediction performances. Additional clinical - and even multimodal - parameters could identify more subtle physiological modifications.
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| http://dx.doi.org/10.1016/j.eplepsyres.2021.106774 | DOI Listing |
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