Neurostimulator devices using a machine learning method implementing a gaussian process optimization

US9931508B2 · kind B2 · utility

Assignees

• CALIFORNIA INSTITUTE OF TECHNOLOGY · US
• University of Louisville Research Foundation, Inc. · US
• The Regents of the University of California · US
• NEURORECOVERY TECHNOLOGIES, INC. · US

Inventors

• Joel W. Burdick · Pasadena, US
• Yu-Chong Tai · Pasadena, US
• John Naber · Prospect, US
• Robert S. Keynton · Louisville, US
• Victor Reggie Edgerton · Los Angeles, US
• Roland R. Roy · Culver City, US
• Yury Gerasimenko · Los Angeles, US
• Susan J. Harkema · Louisville, US
• Jonathan Hodes · Louisville, US
• Claudia A. Angeli · Louisville, US
• Mandheerej S. Nandra · Pasadena, US
• Thomas Anthony Desautels · Pasadena, US
• Steven L. Upchurch · Louisville, US
• Douglas J. Jackson · New Albany, US
• Nicholas A. Terrafranca · Laguna Niguel, US
• Yangsheng Chen · Louisville, US

Key dates

• Filing date: Jun 30, 2016
• Grant date: Apr 3, 2018
• Expiry date: Jun 30, 2036

Classification

• Technology area (CPC A): Human Necessities
• CPC primary: A61B2505/09
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

Neurostimulator devices are described comprising: a stimulation assembly connectable to a plurality of electrodes, wherein the plurality of electrodes are configured to stimulate a spinal cord; one or more sensors; and at least one processor configured to modify at least one complex stimulation pattern deliverable by the plurality of electrodes by integrating data from the one or more sensors and performing a machine learning method implementing a Gaussian Process Optimization on the at least one complex stimulation pattern. Methods of use are also described.