A sparse multiscale nonlinear autoregressive model for seizure prediction.
J Neural Eng
; 18(2)2021 02 26.
Article
en En
| MEDLINE
| ID: mdl-33470981
ABSTRACT
Objectives.Accurate seizure prediction is highly desirable for medical interventions such as responsive electrical stimulation. We aim to develop a classification model that can predict seizures by identifying preictal states, i.e. the precursor of a seizure, based on multi-channel intracranial electroencephalography (iEEG) signals.Approach.A two-level sparse multiscale classification model was developed to classify interictal and preictal states from iEEG data. In the first level, short time-scale linear dynamical features were extracted as autoregressive (AR) model coefficients; arbitrary (usually long) time-scale linear and nonlinear dynamical features were extracted as Laguerre-Volterra AR model coefficients; root-mean-square error of model prediction was used as a feature representing model unpredictability. In the second level, all features were fed into a sparse classifier to discriminate the iEEG data between interictal and preictal states.Main results. The two-level model can accurately classify seizure states using iEEG data recorded from ten canine and human subjects. Adding arbitrary (usually long) time-scale and nonlinear features significantly improves model performance compared with the conventional AR modeling approach. There is a high degree of variability in the types of features contributing to seizure prediction across different subjects.Significance. This study suggests that seizure generation may involve distinct linear/nonlinear dynamical processes caused by different underlying neurobiological mechanisms. It is necessary to build patient-specific classification models with a wide range of dynamical features.
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Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Convulsiones
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Electroencefalografía
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
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Risk_factors_studies
Límite:
Animals
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Humans
Idioma:
En
Año:
2021
Tipo del documento:
Article