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: Devices and methods to effectuate waveform distortion adjusted stimulation therapy of tissue, based on an inverse model of the tissue, are disclosed. In some aspects a linear model of an electrode used to delivery therapy and of the tissue is determined. Parameter values of the linear model may be determined based on measuring voltage feedback during application of stimulation and/or measurement waveforms. The parameter values of the linear model of the electrode and tissue may be used to determine a forward model of the electrical response of the tissue. From the forward model, an inverse model of the tissue may be determined and the inverse model can be used to modify or filter a stimulation waveform to reduce waveform distortion caused by tissue, such as tissue primitive and/or tissue capacitance. Accordingly, providing therapy with the filtered waveform may have increased efficacy by accounting for and adjusting for waveform distortion.

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: 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 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: 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: Devices and methods to effectuate closed loop electrical stimulation of nerve tissue, based on feedback data, to mitigate pain of a patient are disclosed. Feedback data corresponding to bioelectric signals of neurons stimulated by stimulation pulses may be received and analyzed. Based on receipt of the feedback data, it may be determined to modify one or more stimulation parameters, corresponding to the stimulation pulses, to enhance an efficacy of the stimulation pulses at blocking generation and/or propagation of one or more pain signals through a neuroanatomy of the patient. Subsequent and additional stimulation pulses may be provided based on a modified set of stimulation parameters and configured to enhance attenuation of generation and/or transmission of pain signals through the neuroanatomy of the patient to ultimately reduce a level of pain experienced by the patient.

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: Systems and methods are disclosed for conducting spinal cord stimulation or other neurostimulation and sensing evoked compound action potential (ECAP) signals. The sensed signals may be processed to isolate ECAP features from noise and/or interfering signals. The isolated ECAP features may be used to control neurostimulation therapy for the patient and/or guide an implant procedure.

Abstract: Systems and methods are disclosed for conducting spinal cord stimulation or other neurostimulation and sensing evoked compound action potential (ECAP) signals. The sensed signals may be processed to isolate ECAP features from noise and/or interfering signals. The isolated ECAP features may be used to control neurostimulation therapy for the patient and/or guide an implant procedure.

Abstract: Systems and methods are disclosed for conducting spinal cord stimulation or other neurostimulation and sensing evoked compound action potential (ECAP) signals. The sensed signals may be processed to isolate ECAP features from noise and/or interfering signals. The isolated ECAP features may be used to control neurostimulation therapy for the patient and/or guide an implant procedure.

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: In some embodiments, a method of providing a neurostimulation therapy to a patient, comprises: generating a noise pulse pattern defining a pulse train of pulses to be generated according to a noise profile in an external device; communicated the generated noise pulse pattern to an implantable pulse generator (IPG) of a patient; generating, by the IPG, a series of pulses in sequence for noise stimulation of the patient using the noise pulse pattern from the external device, wherein the IPG applies one or more randomization operations to the pulse pattern from the external device without expanding memory storage for the pulse pattern while maintaining the noise profile of the pulse pattern from the external device; and applying the series of pulses in sequence to neural tissue of the patient using one or more electrodes of one or more stimulation leads.

Abstract: A system and method for modulating delivery of remote therapy to a patient having an implantable medical device (IMD). Upon establishing a remote care session between a patient controller device and a clinician programmer, wherein the clinician and the patient are remotely located with respect to each other, a determination may be made if a first triggering event is detected. Responsive to the determination, a remote therapy session used for programming the patient's IMD is paused and one or more remote care therapy setting controls provided at the clinician programmer device to facilitate one or more adjustments with respect to the patient's IMD are disabled. Subsequently, responsive to detecting a second triggering event, the remote therapy session with the patient may be resumed, wherein the one or more remote care therapy setting controls of the clinician programmer device are enabled.

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.