Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: The present disclosure is directed to providing digital health services. In some embodiments, systems and methods for conducting virtual or remote sessions between patients and clinicians are disclosed. During the sessions, media content (e.g., images, video content, audio content, etc.) may be captured as the patient performs one or more tasks. The media content may be presented to the clinician and used to evaluate a condition of the patient or a state of the condition, adjust treatment parameters, provide therapy, or other operations to treat the patient. The analysis of the media content may be aided by one or more machine learning/artificial intelligence models that analyze various aspects of the media content, augment the media content, or other functionality to aid in the treatment of the patient.

Abstract: Systems and methods which provide for and enable sensing responsive signals with respect to the application of paresthesia-free stimulation are described. Sensing signal initiators may be utilized comprising one or more non-therapeutic and/or non-tonic pulses in the form of pinging-pulses configured for invoking responsive signals suitable for measurement and/or analysis in association with the application of neural stimuli. A sensing signal initiator technique may provide an interleaved implementation to introduce one or more pinging-pulses between burst groups of a burst stimulation regimen. Additionally or alternatively, a sensing signal initiator technique may provide a postfixed implementation to introduce one or more pinging-pulses by modifying a therapeutic stimulation burst so that the last phase of the passive discharge is replaced with pinging-pulse providing an active discharge.

Abstract: A sacral lead system including a sacral lead configured to for insertion within a sacral foramen of a patient. The sacral lead supports one or more electrodes which may be configured as one or more stimulation electrodes and/or one or more sensing electrodes. The sacral lead is configured to deliver a stimulation signal to a patient using at least one stimulation electrode and sense an evoked signal produced in response to the stimulation signal using at least one sensing electrode. The sacral lead system may be configured to position the at least one stimulation electrode and/or the at least one sensing electrode within, dorsal, or ventral to the sacral foramen. The sacral lead system may include stimulation circuitry configured to generate the stimulation signal and sensing circuitry configured to receive a signal indicative of the evoked signal.

Abstract: An example method includes delivering, via an electrical stimulation device, one or more electrical stimulation signals to a patient, sensing one or more stimulation-evoked signals that are evoked by stimulation of nerves or muscles of the patient due to the delivery of the one or more electrical stimulation signals, determining a quality of the one or more stimulation-evoked signals, and outputting, based on the quality of the one or more sensed stimulation-evoked signals being below a quality threshold, one or more instructions to improve the quality of one or more subsequent stimulation-evoked signals.

Abstract: An example method includes delivering one or more electrical stimulation signals to a patient, sensing a composite stimulation-evoked signal comprising a composite of signals generated by one or more signal sources in response to the one or more electrical stimulation signals, and controlling delivery of electrical stimulation therapy to the patient based on the composite stimulation-evoked signal.

Abstract: A system and method for facilitating DBS electrode trajectory planning using a machine learning (ML)-based feature identification scheme configured to identify and distinguish between various regions of interest (ROIs) and regions of avoidance (ROAs) in a patient's brain scan image. In one arrangement, standard orientation image slices as well as re-sliced images in non-standard orientations are provided in a labeled input dataset for training a CNN/ANN for distinguishing between ROIs and ROAs. Upon identification of the ROIs and ROAs in the patient's brain scan image, an optimal trajectory for implanting a DBS lead may be determined relative to a particular ROI while avoiding any ROAs.

Abstract: The present disclosure provides systems and methods for estimating an orientation of an implanted deep brain stimulation (DBS) lead. Such methods include generating an initial image dataset, down-sampling a respective image or adding noise to images of the subset of the initial image dataset, and re-slicing at least a subset of the modified image dataset along an alternative primary imaging axis, to generate an integrated image dataset. The method also include partitioning the integrated image dataset into a preliminary training image dataset and a testing image dataset, and re-sizing at least a subset of the preliminary training image dataset with a localized field of view around a depicted DBS lead, to generate a training image dataset. The method further includes training a machine-learning model using the training image dataset, and executing the trained machine-learning model to estimate, during a DBS implantation procedure, an orientation of a subject implanted DBS lead.

Abstract: Provided herein is a computing system for optimizing a waveform, in communication with an implantable pulse generator, and including a computing device including a memory device and a processor communicatively coupled to the memory device. The processor is configured to: retrieve historical waveform data associated with a plurality of waveforms used in therapeutic sessions for a plurality of patients, the historical waveform data including a plurality of waveform parameters; analyzing the historical waveform data to determine preferred waveform parameters; determining that a patient is starting a new therapeutic session using the patient therapeutic device; displaying each of the preferred waveform parameters; prompting the user to accept or modify the displayed waveform parameters; optimizing the waveform parameters for the therapeutic session; and transmitting the optimized waveform parameters to the patient therapeutic device to start the therapeutic session.

Abstract: The present disclosure provides systems and methods for generating pulsed waveforms that approximate colored noise for use in a neurostimulation system. An implantable neurostimulation system includes an implantable stimulation lead including a plurality of contacts, and an implantable pulse generator communicatively coupled to the stimulation lead. The pulse generator is configured to generate a pulsed waveform that approximates colored noise using at least one of a thresholding method and an optimization method, and cause stimulation to be delivered by the stimulation lead based on the pulsed waveform.

Abstract: The present disclosure provides systems and methods for generating burst waveforms. An implantable neurostimulation system includes an implantable stimulation lead including a plurality of contacts, and an implantable pulse generator communicatively coupled to the stimulation lead. The pulse generator is configured to generate a waveform including a burst that includes a leading anodic pulse followed by alternating cathodic pulses and anodic pulses, each cathodic pulse in the burst having a greater amplitude than the previous cathodic pulse.

Abstract: Described here are bioelectric modulation systems and methods for generating rotating or spatially-selective electromagnetic fields. A modulation system includes a multichannel electrode with independently controllable electrode channels that can be operated to generate rotating electromagnetic fields that stimulate cells regardless of their orientation, or to generate spatially-selective electromagnetic fields that preferentially stimulate cells oriented along a particular direction. The bioelectric modulation system may be implemented for stimulation of neurons or other electrically active cells. The bioelectric modulation described here may be used for a variety applications including deep brain stimulation (DBS), spinal cord and vagus nerve stimulation, stimulation of myocardial (heart) tissue, and directional stimulation of muscles.

Abstract: The present disclosure provides systems and methods for generating stimulation patterns. A computing device includes a processor, and a memory device communicatively coupled to the processor. The memory device includes instructions that, when executed, cause the processor to provide a plurality of inputs to a multi-objective modified binary particle swarm optimization (MOMBPSO) algorithm, and apply the MOMBPSO to a computational circuit model using the plurality of inputs to generate a plurality of candidate stimulation patterns, wherein the MOMBPSO is applied to the computational circuit model to optimize both i) therapy efficacy and ii) power utilization.

Abstract: Systems and methods for programming multi-electrode neuromodulation systems, with applications for at least deep brain stimulation (DBS) therapy. One or more configurations can be generated and presented to a user for selection. The selected configurations can be settings for programming a pulse generator. The one or more configurations can be Pareto optimal in terms of one or more objective values and can be generated using a particle swarm optimization. The generated configurations can be visualized on a Pareto front for user selection. Objective values can include minimizing power use, maximizing activation of neural pathways and/or regions of interest, minimizing activation of neural pathways and or regions of avoidance, and maximizing or minimizing the distance to sources of sensed functional data.

Abstract: Systems and methods for programming multi-electrode neuromodulation systems, with applications for at least deep brain stimulation (DBS) therapy. One or more configurations can be generated and presented to a user for selection. The selected configurations can be settings for programming a pulse generator. The one or more configurations can be Pareto optimal in terms of one or more objective values and can be generated using a particle swarm optimization. The generated configurations can be visualized on a Pareto front for user selection. Objective values can include minimizing power use, maximizing activation of neural pathways and/or regions of interest, minimizing activation of neural pathways and or regions of avoidance, and maximizing or minimizing the distance to sources of sensed functional data.

Abstract: Described here are bioelectric modulation systems and methods for generating rotating or spatially-selective electromagnetic fields. A modulation system includes a multichannel electrode with independently controllable electrode channels that can be operated to generate rotating electromagnetic fields that stimulate cells regardless of their orientation, or to generate spatially-selective electromagnetic fields that preferentially stimulate cells oriented along a particular direction. The bioelectric modulation system may be implemented for stimulation of neurons or other electrically active cells. The bioelectric modulation described here may be used for a variety applications including deep brain stimulation (DBS), spinal cord and vagus nerve stimulation, stimulation of myocardial (heart) tissue, and directional stimulation of muscles.