US-12623079-B1 - Systems and methods for providing neurostimulation therapy according to gait features

Abstract

The present disclosure provides systems and methods for providing neurostimulation therapy according to gait features. The gait features may be analyzed to develop a patient model between gait features and optimal settings for a neurostimulation therapy using machine learning operations. The model is used to control ongoing neurostimulation therapy for the patient.

Inventors

• Anahita KYANI
• Erika Ross

Assignees

• ADVANCED NEUROMODULATION SYSTEMS, INC.

Dates

Publication Date

20260512

Application Date

20221207

Claims (10)

1. 1 . A method of providing a neurostimulation therapy to a patient, comprising: applying electrical pulses to a neural target of a patient according to a plurality of stimulation parameters; obtaining movement data from one or more sensors implanted in or worn by the patient while electrical pulses are applied to the patient; processing the movement data to extract a plurality of gait features corresponding to characteristics of the patient's gait while electrical pulses are applied to the patient; obtaining patient reported pain levels from the patient using one or more patient applications on a patient therapy controller device corresponding to time intervals in which the electrical pulses are applied to the patient; training a machine learning (ML) model using the plurality of stimulation parameters, the plurality of gait features, and the patient reported pain levels; after training the ML model, controlling application of electrical pulses to the patient to treat pain of the patient in accordance with extracted gait features of the patient; determining a sensor-driven score associated with the patient; determining an evaluator-driven score associated with the patient based on patient reported outcomes; determining a rigidity score associated with the patient based on the sensor-driven score and the evaluator-driven score; modifying the plurality of stimulation parameters based on the rigidity score; and applying electrical pulses to the neural target of the patient according to the modified plurality of stimulation parameters.
2. 2 . The method of claim 1 , wherein the neurostimulation therapy is selected from the list consisting of: spinal cord stimulation and dorsal root ganglion stimulation.
3. 3 . The method of claim 1 , wherein movement data is obtained by one or more sensors of a wearable device of the patient.
4. 4 . The method of claim 1 , wherein movement data is obtained by one or more sensors of an implantable pulse generator (IPG) of the patient.
5. 5 . The method of claim 1 , wherein the training is performed by one or more software applications executed on the patient therapy controller device.
6. 6 . The method of claim 1 , wherein the training is performed by one or software applications executed by one or more processors of an implantable pulse generator (IPG) of the patient.
7. 7 . The method of claim 1 , wherein the training is performed by one or software applications executed by one or more processors of a server platform of a medical device management system.
8. 8 . The method of claim 1 , wherein one or more features associated with the training include at least one feature selected from the list consisting of: step length, stride length, stance phase, swing phase, single support, total double support, load response, pre-swing, step time, gait cycle, cadence, and speed.
9. 9 . The method of claim 1 , wherein: the sensor-driven score is determined based on the movement data from the one or more sensors implanted in or worn by the patient; the evaluator-driven score is determined based on the patient reported outcomes and an assessment performed by a clinician, the patient reported outcomes including the patient reported pain levels, patient reported well-being scores, and patient reported activity levels; and the rigidity score represents a quantified measure of involuntary muscle tone or resistance to movement in one or more regions of the patient's body.
10. 10 . The method of claim 1 , further comprising: obtaining the patient reported outcomes from the patient using the one or more patient applications on the patient therapy controller device, the patient reported outcomes including the patient reported pain levels, patient reported well-being scores, and patient reported activity levels, wherein the training the ML model includes training the ML model using the plurality of stimulation parameters, the plurality of gait features, and the patient reported outcomes.

Description

CROSS-REFERENCE TO RELATED APPLICATION(S) The present application claims the benefit of priority from U.S. Provisional Patent Application No. 63/287,128, entitled SYSTEMS AND METHODS FOR PROVIDING NEUROSTIMULATION THERAPY ACCORDING TO GAIT FEATURES and filed Dec. 8, 2021, the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD The present application is generally directed to providing digital health services to patients. BACKGROUND Implantable medical devices have changed how medical care is provided to patients having a variety of chronic illnesses and disorders. For example, implantable cardiac devices improve cardiac function in patients with heart disease by improving quality of life and reducing mortality rates. Respective types of implantable neurostimulators provide a reduction in pain for chronic pain patients and reduce motor difficulties in patients with Parkinson's disease and other movement disorders. A variety of other medical devices are proposed and are in development to treat other disorders in a wide range of patients. Many implantable medical devices and other personal medical devices are programmed by a physician or other clinician to optimize the therapy provided by a respective device to an individual patient. Typically, the programming occurs using short-range communication links (e.g., inductive wireless telemetry) in an in-person or in-clinic setting. Since such communications typically require close immediate contact, there is only an extremely small likelihood of a third-party establishing a communication session with the patient's implanted device without the patient's knowledge. Remote patient care is a healthcare delivery method that aims to use technology to provide patient health outside of a traditional clinical setting (e.g., in a doctor's office or a patient's home). It is widely expected that remote patient care may increase access to care and decrease healthcare delivery costs. SUMMARY The present application is generally directed to providing a neurostimulation therapy according to gait features of the patient. BRIEF DESCRIPTION OF THE DRAWINGS Embodiments of the present disclosure are illustrated by way of example, and not by way of limitation, in the Figures of the accompanying drawings in which like references indicate similar elements. It should be noted that different references to “an” or “one” embodiment in this disclosure are not necessarily to the same embodiment, and such references may mean at least one. Further, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to effectuate such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. The accompanying drawings are incorporated into and form a part of the specification to illustrate one or more exemplary embodiments of the present disclosure. Various advantages and features of the disclosure will be understood from the following Detailed Description taken in connection with the appended claims and with reference to the attached drawing Figures in which: FIG. 1A depicts an example architecture of a system configured to support remote patient therapy as part of an integrated remote care service session in a virtual clinic environment that may be deployed in a cloud-centric digital health implementation according to one or more embodiments of the present patent disclosure; FIG. 1B depicts an example network environment wherein the remote care service architecture of FIG. 1A may be implemented according to a representative embodiment; FIG. 2 depicts a flowchart illustrative of blocks, steps and/or acts that may be (re)combined in one or more arrangements with or without additional flowcharts of the present disclosure for facilitating remote care therapy in a secure network environment for purposes of some embodiments; FIG. 3 depicts a flowchart illustrative of blocks, steps and/or acts that may be implemented for establishing a communication session with an implantable medical device; FIGS. 4A and 4B depicts flowcharts illustrative of a remote care scenario involving an example digital health network architecture wherein an integrated remote care session may be established between a patient and a clinician operating respective controller devices for purposes of some embodiments of the present disclosure; FIGS. 5A and 5B depict representations of an example user interface and associated dialog boxes provided with a clinician programmer device for selecting different therapy applications and/or service modes for purposes of some embodiments of the present disclosure; FIG. 6 depicts a representation of an example user interface provided with a clinician programmer device for facilitating controls with respect to an AV communication session and a remote therapy session in an integrated remote care service