US 12,433,527 B1
Predicting patient responses to multiple modalities of CNS disease interventions
Qing Wang, Palo Alto, CA (US); and Qiang Li, Saratoga, CA (US)
Assigned to Neumarker Inc., Palo Alto, CA (US)
Filed by Neumarker Inc., Palo Alto, CA (US)
Filed on Jul. 9, 2024, as Appl. No. 18/766,919.
Claims priority of provisional application 63/575,542, filed on Apr. 5, 2024.
Int. Cl. A61B 5/372 (2021.01); G16H 50/20 (2018.01)
CPC A61B 5/372 (2021.01) [G16H 50/20 (2018.01)] 26 Claims
OG exemplary drawing
 
1. An electroencephalography (EEG) system comprising:
an EEG device;
a display;
one or more processors;
a memory; and
one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for:
receiving measurement data collected by a medical device from a patient diagnosed with a central nervous system (CNS) disease, wherein the measurement data comprises EEG data;
extracting, from the measurement data, a first set of features obtained using a first set of electrode channels corresponding to a first set of recording sites on the patient's scalp, wherein the first set of recording sites are selected based on a first candidate treatment of the CNS disease;
extracting, from the measurement data, a second set of features obtained using a second set of electrode channels corresponding to a second set of recording sites on the patient's scalp, wherein the second set of recording sites are selected based on a second candidate treatment of the CNS disease and are different from the first set of recording sites;
inputting the first set of features into a first treatment-specific machine-learning model to predict a first treatment response by the patient to the first candidate treatment of the CNS disease, wherein the first treatment-specific machine-learning model is configured to predict patient responses to the first candidate treatment;
inputting the second set of features into a second treatment-specific machine-learning model to predict a second treatment response by the patient to the second candidate treatment of the CNS disease, wherein the second treatment-specific machine-learning model is configured to predict patient responses to the second candidate treatment;
generating a data structure based on the predicted first treatment response to the first candidate treatment of the CNS disease and the predicted second treatment response to the second candidate treatment of the CNS disease; and
rendering, on the display, the generated data structure to provide the predicted first treatment response to the first candidate treatment of the CNS disease and the predicted second treatment response to the second candidate treatment of the CNS disease.