SEIZURE DETECTION, PREDICTION AND PREVENTION USING NEUROSTIMULATION TECHNOLOGY AND DEEP NEURAL NETWORK

A method for neuromodulation includes monitoring brain activity of a patient using one or more electrodes attached to the patient, and using a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient. The method also includes,...

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Bibliographic Details
Main Authors Saha, Susmita, Roy, Subhrajit, Kiral-Kornek, Filiz Isabell, Harrer, Stefan, Mashford, Benjamin Scott
Format Patent
LanguageEnglish
Published 30.05.2019
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
DIAGNOSIS
ELECTROTHERAPY
HUMAN NECESSITIES
HYGIENE
IDENTIFICATION
MAGNETOTHERAPY
MEDICAL OR VETERINARY SCIENCE
PHYSICS
RADIATION THERAPY
SURGERY
ULTRASOUND THERAPY
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Summary:A method for neuromodulation includes monitoring brain activity of a patient using one or more electrodes attached to the patient, and using a first machine learning model to predict whether a patient will have a seizure based on the monitored brain activity of the patient. The method also includes, responsive to the first machine learning model predicting that the patient will have a seizure, using a second machine learning model to determine a neuromodulation signal pattern for preventing the predicted seizure. The method further includes using a neurostimulator to apply the determined neuromodulation signal pattern to the patient. The method also includes, after applying the determined neuromodulation signal pattern to the patient, detecting whether the patient had the predicted seizure based on the monitored brain activity of the patient. The method further includes adjusting at least the second machine learning model based on whether the patient had the predicted seizure.
Bibliography:Application Number: US201715828409