University of Minnesota

Logan L Grado

Theoden Ivan Netoff

Matthew D Johnson

Bryan Moore

Andy Lamperski

Systems and methods for adaptively controlling an electrical stimulation device, such as a closed-loop stimulation device, based in part on a Bayesian optimization of the operational parameters of the device are described. An adaptive dual control of the stimulation device can be provided. In a first control loop parameters are extracted from signals recorded from the subject by the stimulation device, and in a second control loop a Bayesian optimization is implemented with a hardware processor and memory to compute updated operational parameters for the stimulation device. As noted, the stimulation device is an electrical stimulation device, and may be a closed-loop stimulation device. Such devices can be used for deep brain stimulation ('DBS'), cardiac resynchronization therapy (“CRT”), and other electrophysiological stimulation applications.

Method for adaptive control of a medical device using Bayesian optimization

Systems and methods for digital signal processing using a sliding windowed infinite Fourier transform ('SWIFT') algorithm are described. A discrete-time Fourier transform ('DTFT') of an input signal is computed over an infinite-length temporal window that is slid from one sample in the input signal to the next. The DTFT with the temporal window at a given sample point is effectively calculated by phase shifting and decaying the DTFT calculated when the temporal window was positioned at the previous sample point and adding the current sample to the result.

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Minneapolis, MN, United States of America

Logan L Grado