Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.

Abstract: 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.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using a three-dimensional (3D) grid comprising a plurality of voxels that are each assigned a value. The processor may register the VTA with the 3D grid and determine the score for the therapy program based on the values assigned to voxels with which the VTA overlaps. One or more therapy programs for electrical stimulation therapy (e.g., deep brain stimulation) may be selected based on the scores determined based on the 3D grid.

Abstract: 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.

Abstract: 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.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.

Abstract: A therapeutic window for at least one electrode of a medical system may be determined based on a volume of tissue expected to be activated (“VTA”) by electrical stimulation delivered by the at least one electrode. In some examples, a processor determines the therapeutic window for a particular electrode by at least determining an efficacy threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the efficacy threshold, and determining an adverse-effects threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the adverse-effects threshold.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using a three-dimensional (3D) grid comprising a plurality of voxels that are each assigned a value. The processor may register the VTA with the 3D grid and determine the score for the therapy program based on the values assigned to voxels with which the VTA overlaps. One or more therapy programs for electrical stimulation therapy (e.g., deep brain stimulation) may be selected based on the scores determined based on the 3D grid.

Abstract: 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.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using a three-dimensional (3D) grid comprising a plurality of voxels that are each assigned a value. The processor may register the VTA with the 3D grid and determine the score for the therapy program based on the values assigned to voxels with which the VTA overlaps. One or more therapy programs for electrical stimulation therapy (e.g., deep brain stimulation) may be selected based on the scores determined based on the 3D grid.

Abstract: A therapeutic window for at least one electrode of a medical system may be determined based on a volume of tissue expected to be activated (“VTA”) by electrical stimulation delivered by the at least one electrode. In some examples, a processor determines the therapeutic window for a particular electrode by at least determining an efficacy threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the efficacy threshold, and determining an adverse-effects threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the adverse-effects threshold.

Abstract: A therapeutic window for at least one electrode of a medical system may be determined based on a volume of tissue expected to be activated (“VTA”) by electrical stimulation delivered by the at least one electrode. In some examples, a processor determines the therapeutic window for a particular electrode by at least determining an efficacy threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the efficacy threshold, and determining an adverse-effects threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the adverse-effects threshold.

Abstract: A therapeutic window for at least one electrode of a medical system may be determined based on a volume of tissue expected to be activated (“VTA”) by electrical stimulation delivered by the at least one electrode. In some examples, a processor determines the therapeutic window for a particular electrode by at least determining an efficacy threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the efficacy threshold, and determining an adverse-effects threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the adverse-effects threshold.

Abstract: A therapeutic window for at least one electrode of a medical system may be determined based on a volume of tissue expected to be activated (“VTA”) by electrical stimulation delivered by the at least one electrode. In some examples, a processor determines the therapeutic window for a particular electrode by at least determining an efficacy threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the efficacy threshold, and determining an adverse-effects threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the adverse-effects threshold.

Abstract: In one example, a method includes selecting, by one or more processors and based on a representation of sensed electrical signals for a particular patient and a plurality of representations of sensed electrical signals for a plurality of other patients, a combination of electrodes of a plurality of combinations of one or more implantable electrodes for delivery of electrical stimulation to the particular patient.

Abstract: A therapeutic window for at least one electrode of a medical system may be determined based on a volume of tissue expected to be activated (“VTA”) by electrical stimulation delivered by the at least one electrode. In some examples, a processor determines the therapeutic window for a particular electrode by at least determining an efficacy threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the efficacy threshold, and determining an adverse-effects threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the adverse-effects threshold.

Abstract: In one example, a method includes selecting, by one or more processors and based on a representation of sensed electrical signals for a particular patient and a plurality of representations of sensed electrical signals for a plurality of other patients, a combination of electrodes of a plurality of combinations of one or more implantable electrodes for delivery of electrical stimulation to the particular patient.

Abstract: A therapeutic window for at least one electrode of a medical system may be determined based on a volume of tissue expected to be activated (“VTA”) by electrical stimulation delivered by the at least one electrode. In some examples, a processor determines the therapeutic window for a particular electrode by at least determining an efficacy threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the efficacy threshold, and determining an adverse-effects threshold based on the VTA expected to result from the delivery of electrical stimulation according to a set of electrical stimulation parameter values including the stimulation parameter at the adverse-effects threshold.

Abstract: In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated (“VTA”) by therapy delivery according to the therapy program. The score may be determined using a three-dimensional (3D) grid comprising a plurality of voxels that are each assigned a value. The processor may register the VTA with the 3D grid and determine the score for the therapy program based on the values assigned to voxels with which the VTA overlaps. One or more therapy programs for electrical stimulation therapy (e.g., deep brain stimulation) may be selected based on the scores determined based on the 3D grid.