Abstract: A seizure detection system including one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, determine to switch from identifying the candidate seizures with the statistical analysis to an artificial intelligence model, and switch from identifying the candidate seizures with the statistical analysis to identifying the candidate seizures based on the artificial intelligence model with the EEG signal.

Abstract: A seizure detection system including one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, determine to switch from identifying the candidate seizures with the statistical analysis to an artificial intelligence model, and switch from identifying the candidate seizures with the statistical analysis to identifying the candidate seizures based on the artificial intelligence model with the EEG signal.

Abstract: A seizure detection system including one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to identify candidate seizures with a statistical analysis that identifies the candidate seizures based on changes in non-linear features of the EEG signal, determine to switch from identifying the candidate seizures with the statistical analysis to an artificial intelligence model, and switch from identifying the candidate seizures with the statistical analysis to identifying the candidate seizures based on the artificial intelligence model with the EEG signal.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient and determine different types of metrics based on the EEG signal, the different types of metrics indicating non-linear features of the EEG signal. The one or more circuits are configured to determine whether one or more of the different types of metrics exhibit changes over time that meet a predefined level of statistical significance, generate a user interface, the user interface including a real-time trend of the EEG signal and the one or more of the different types of metrics, and cause a user interface device to display the user interface.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.

Abstract: A seizure detection system including one or more circuits, the one or more circuits configured to receive an electroencephalogram (EEG) signal generated based on electrical brain activity of a patient. The one or more circuits are configured to determine metrics based on the EEG signal, the metrics indicating non-linear features of the EEG signal, determine that the EEG signal indicates a candidate seizure by determining, based at least in part on the metrics, a change in the non-linear features of the EEG signal over time, and generate a seizure alert indicating that the EEG signal indicates the candidate seizure. The change in the non-linear features indicates a physiological force that gives rise to the candidate seizure.