US 12,333,443 B2
Systems and methods for using federated learning for training centralized seizure detection and prediction models on decentralized datasets
Sharanya Arcot Desai, Sunnyvale, CA (US); and Thomas K. Tcheng, Pleasant Hill, CA (US)
Assigned to NeuroPace, Inc, Mountain View, CA (US)
Filed by NeuroPace, Inc., Mountain View, CA (US)
Filed on Jun. 23, 2021, as Appl. No. 17/356,342.
Claims priority of provisional application 63/043,514, filed on Jun. 24, 2020.
Prior Publication US 2021/0407678 A1, Dec. 30, 2021
Int. Cl. G06N 3/09 (2023.01); G06F 8/65 (2018.01); G06N 3/098 (2023.01); G16H 50/20 (2018.01)
CPC G06N 3/098 (2023.01) [G06F 8/65 (2013.01); G16H 50/20 (2018.01)] 29 Claims
OG exemplary drawing
 
1. A method of replacing a first machine learning model of a first architecture resident in a plurality of implanted medical devices, wherein the first machine learning model is generated using a first dataset and is configured to detect a neurological event in electrical activity of a brain, the method comprising:
providing, from a server to each of a plurality of remote sources remote from the server, information on a second dataset for generating a second machine learning model of a second architecture different than the first architecture, which second dataset includes at least one type of data that is not included in the first dataset and comprises records of electrical activity collected in response to detections of neurological events by the first machine learning model;
generating, at each of the plurality of remote sources, a version of the second machine learning model based on a corresponding second dataset;
receiving, at a server, a plurality of versions of the second machine learning model from the plurality of remote sources;
aggregating, at the server, the plurality of versions of the second machine learning model to derive a server-generated version of the second machine learning model; and
transmitting, at the server, the server-generated version of the second machine learning model to one or more of the plurality of remote sources as a replacement for the first machine learning model.