Seizure detection, prediction and prevention using neurostimulation technology and deep neural network

An apparatus for neuromodulation, comprising:

• a memory;
• and
• at least one processor coupled to the memory, the processor being operative:
• to monitor brain activity of a patient using one or more electrodes attached to the patient;
• to use a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient;
• responsive to the first machine learning model predicting that the patient will have a seizure, to use a second machine learning model to determine a neuromodulation signal pattern for preventing the predicted seizure;
• to use a neurostimulator to apply the determined neuromodulation signal pattern to the patient;
• after applying the determined neuromodulation signal pattern to the patient, to detect whether the patient had the predicted seizure based on the monitored brain activity of the patient;
• and
• to adjust at least the second machine learning model based on whether the patient had the predicted seizure.

A computer program product comprising a non-transitory machine-readable storage medium having machine-readable program code embodied therewith, said machine-readable program code comprising machine-readable program code configured:

• to monitor brain activity of a patient using one or more electrodes attached to the patient;
• to use a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient;
• responsive to the first machine learning model predicting that the patient will have a seizure, to use a second machine learning model to determine a neuromodulation signal pattern for preventing the predicted seizure;
• to use a neurostimulator to apply the determined neuromodulation signal pattern to the patient;
• after applying the determined neuromodulation signal pattern to the patient, to detect whether the patient had the predicted seizure based on the monitored brain activity of the patient;
• and
• to adjust at least the second machine learning model based on whether the patient had the predicted seizure.