Techniques for pairing an external device such as a clinician programmer (CP) to an implantable medical device (IMD) are disclosed, which involve use of a remote controller (RC) paired to the IMD. The RC is placed into a CP pairing mode, which acts differently depending on the type of IMD paired to the RC. If the IMD is RF based, the CP pairing mode places the IMD in a pairing mode, thus allowing the CP to connect directly with the IMD via a RF telemetry protocol. If the IMD is magnetic-induction based, the CP pairing mode causes the RC to advertise its presence to the CP, allowing the CP to connect to the RC via the RF telemetry protocol. Because the RC is also paired with the IMD via a magnetic induction telemetry protocol, the RC acts as a passthrough device to allow communications between the CP and the IMD.

In some examples, a computing apparatus may determine information corresponding to a data structure and indicating delays associated with an atrium lead, a left ventricle (LV) lead, and a right ventricle (RV) lead based on one or more input variables. The computing apparatus may determine a plurality of individualized characteristics based on the information corresponding to the data structure. The computing apparatus may receive, from the plurality of measurement electrodes, a plurality of second sets of electrical measurements indicating second electrical signals applied to the patient's heart based on the plurality of individualized characteristics. The computing apparatus may determine cardiac resynchronization index (CRI) values using a first set of electrical measurements (e.g., native measurements) and the plurality of second sets of electrical measurements. The computing apparatus may generate a graphical representation based on a populated data structure and cause display of the graphical representation.

A system includes memory configured to store image content representative of a lead implanted within a patient, and processing circuitry. The processing circuitry is configured to determine a reference point in the image content, determine a plane in the image content that corresponds to an orientation marker based on the reference point, determine an orientation of the lead based on the determined plane, and output information indicative of the determined orientation.

The present disclosure provides systems and methods for providing neurostimulation therapy using multi-dimensional patient features. The multi-dimensional patient features may include features in respective frequency bands for selected cortical sites from EEG localization data. Additionally or alternatively, the multi-dimensional patient features may include features from patient physiological data or other patient activity data. The multi-dimensional feature data may be compared against AI/ML models of patient and/or healthy population members. Closed-loop therapy adjustments may be applied to a respective patient’s neurostimulation therapy using the multi-dimensional patient feature analysis.

A computer brain interface (CBI) device of an individual is self-calibrated. A neurostimulation test signal is generated based on a selected set of test signal parameters. The neurostimulation signal is applied to the afferent sensory nerve fibers to elicit a bioelectric response via a neurostimulation interface operably connected to or integrated with the CBI device. The neurostimulation interface senses the bioelectric responses of the stimulated afferent sensory nerve fibers. The CBI devices determines, based on the sensed bioelectric responses, whether an excitation behavior of the stimulated afferent sensory nerve fibers with respect to the neurostimulation interface has changed. When the excitation behavior has changed, a set of recalibrated neurostimulation signal parameters is determined based on the sensed bioelectric responses. The CBI device is operated using the recalibrated neurostimulation signal parameters to communicate information to the individual via neurostimulation of the afferent sensory nerve fibers.

In rehabilitation, a stimulation pattern when applied to a body part by a neuromuscular electrical stimulation (NMES) device is effective to cause the body part to perform an intended action. The applying includes increasing a stimulation level at which the stimulation pattern is applied over time and, during the applying, acquiring video of the body part. The body part is monitored during the applying by analysis of the video, and the applying is automatically stopped in response to the monitoring indicating the body part has performed the intended action. The stimulation pattern may be defined as one or more subsets of electrodes of the NMES device and an electrode group stimulation level for each respective subset of electrodes, and the increasing of the stimulation level comprises increasing a scaling factor applied to the electrode group stimulation levels over time.

An assistance method for assisting a person in grasping or otherwise manipulating an object includes receiving video of a hand of the person and of an object. An intent to grasp the object is identified based on proximity of the hand to the object in the video or as measured by a proximity sensor, or using gaze tracking, or based on measured neural activity of the person. The object and the hand in the video are analyzed to determine an object grasping action for grasping or otherwise manipulating the object. An actuator is controlled to cause the hand to perform the determined hand action for grasping or otherwise manipulating the object.

A medical device may receive sensor data from sensing sources, and determine confidence levels for sensor data received from each of the plurality of sensing sources. Each of the confidence levels of the sensor data from each of the sensing sources is a measure of accuracy of the sensor data received from respective sensing sources. The medical device may also determine one or more therapy parameter values based on the determined confidence levels, and cause delivery of therapy based on the determined one or more therapy parameter values.

An example of a neurostimulation system may include a programming control circuit, a sensing circuit, and a stimulation control circuit. The programming control circuit may be configured to generate stimulation parameters controlling delivery of neurostimulation according to stimulation waveform(s) and stimulation field(s). The sensing circuit may be configured to sense signals. The stimulation control circuit may be configured to determine the stimulation waveform(s) and the stimulation field(s) based on a lead configuration and may be configured to determine first and second electrodes of respective first and second leads, receive first and second signals sensed using the first and second electrodes, detect corresponding signal features from the first and second signals, determine a feature delay between the detected signal features, and determine a need for adjusting the lead configuration using the feature delay. The signal features are associated with a response of the patient to the neurostimulation.

A patient external controller is provided for controlling sub-perception stimulation provided by a patients implantable stimulator device having an electrode array. Control circuitry in the controller renders a graphical user interface (GUI), including a location at which the sub-perception stimulation is provided within the electrode array, and a pre-defined region in which the location can be moved. The pre-defined region may be constrained to less than the entire electrode array. The control circuitry receives one or more first inputs to move the location of the sub-perception stimulation within the region and to program the stimulator to move the sub-perception stimulation to the moved location in the electrode array. The control circuitry can enable adjustment of an amplitude of the sub-perception stimulation to a value that is less than or equal to a perception threshold. Once moved, the sub-perception stimulation an be stored as a second stimulation program.