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: Techniques related to classifying a posture state of a living body are disclosed. One aspect relates to sensing at least one signal indicative of a posture state of a living body. Posture state detection logic classifies the living body as being in a posture state based on the at least one signal, wherein this classification may take into account at least one of posture and activity state of the living body. The posture state detection logic further determines whether the living body is classified in the posture state for at least a predetermined period of time. Response logic is described that initiates a response as a result of the body being classified in the posture state only after the living body has maintained the classified posture state for at least the predetermined period of time. This response may involve a change in therapy, such as neurostimulation therapy, that is delivered to the living body.

Abstract: Techniques are disclosed to automate determination of therapy parameter values for adaptive deep brain stimulation (aDBS). A medical device may determine differences in power values between a present and a previous power value. Based on the difference being greater than or equal to a threshold value, the medical device may iteratively adjust a present therapy parameter value until the difference in the power values between a present and a previous power value is less than the threshold value.

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 patient controls the delivery of therapy through volitional inputs that are detected by a biosignal within the brain. The volitional patient input may be directed towards performing a specific physical or mental activity, such as moving a muscle or performing a mathematical calculation. In one embodiment, a biosignal detection module monitors an electroencephalogram (EEG) signal from within the brain of the patient and determines whether the EEG signal includes the biosignal. In one embodiment, the biosignal detection module analyzes one or more frequency components of the EEG signal. In this manner, the patient may adjust therapy delivery by providing a volitional input that is detected by brain signals, wherein the volitional input may not require the interaction with another device, thereby eliminating the need for an external programmer to adjust therapy delivery. Example therapies include electrical stimulation, drug delivery, and delivery of sensory cues.

Abstract: Intracranial pressure of a patient may be monitored in order to evaluate a seizure disorder. In some examples, trends in the intracranial pressure over time may be monitored, e.g., to detect changes to the patient's condition. In addition, in some examples, a seizure metric may be generated for a detected seizure based on sensed intracranial pressures. The seizure metric may indicate, for example, an average, median, or highest relative intracranial pressure value observed during a seizure, a percent change from a baseline value during the seizure, or the time for the intracranial pressure to return to a baseline state after the occurrence of a seizure. In addition to or instead of intracranial pressure, patient motion or posture may be monitored in order to assess the patient's seizure disorder. For example, a seizure type or severity may be determined based on patient motion sensed during a seizure.

Abstract: A patient controls the delivery of therapy through volitional inputs that are detected by a biosignal within the brain. The volitional patient input may be directed towards performing a specific physical or mental activity, such as moving a muscle or performing a mathematical calculation. In one embodiment, a biosignal detection module monitors an electroencephalogram (EEG) signal from within the brain of the patient and determines whether the EEG signal includes the biosignal. In one embodiment, the biosignal detection module analyzes one or more frequency components of the EEG signal. In this manner, the patient may adjust therapy delivery by providing a volitional input that is detected by brain signals, wherein the volitional input may not require the interaction with another device, thereby eliminating the need for an external programmer to adjust therapy delivery. Example therapies include electrical stimulation, drug delivery, and delivery of sensory cues.

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 system for detecting strokes includes a sensor device configured to obtain physiological data from a patient, for example brain activity data. A computing device communicatively coupled to the sensor device is configured to receive the physiological data and compare it with reference data. The reference data can be patient data from an opposite brain hemisphere to the hemisphere being interrogated or the reference data can be non-patient data from stroke and normal patient populations. Based on comparison of the physiological data and the reference data, the system indicates whether the patient has suffered a stroke.

Abstract: Techniques are disclosed for defining a homeostatic window for controlling delivery of electrical stimulation therapy to a patient. In one example, a method includes generating and delivering electrical stimulation therapy to tissue of a patient via electrodes. Further, the method includes adjusting a level of a parameter of the electrical stimulation therapy such that a signal of the patient is not less than a lower bound and not greater than an upper bound. The lower bound is determined to be the magnitude of the signal while receiving electrical stimulation therapy sufficient to reduce one or more symptoms of a disease while the patient was receiving medication for reduction of the one or more symptoms. Further, the upper bound is determined to be the magnitude of the signal while receiving electrical stimulation therapy sufficient to reduce the one or more symptoms when the patient was not receiving the medication.

Abstract: Techniques related to classifying a posture state of a living body are disclosed. One aspect relates to sensing at least one signal indicative of a posture state of a living body. Posture state detection logic classifies the living body as being in a posture state based on the at least one signal, wherein this classification may take into account at least one of posture and activity state of the living body. The posture state detection logic further determines whether the living body is classified in the posture state for at least a predetermined period of time. Response logic is described that initiates a response as a result of the body being classified in the posture state only after the living body has maintained the classified posture state for at least the predetermined period of time. This response may involve a change in therapy, such as neurostimulation therapy, that is delivered to the living body.

Abstract: Techniques related to classifying a posture state of a living body are disclosed. One aspect relates to sensing at least one signal indicative of a posture state of a living body. Posture state detection logic classifies the living body as being in a posture state based on the at least one signal, wherein this classification may take into account at least one of posture and activity state of the living body. The posture state detection logic further determines whether the living body is classified in the posture state for at least a predetermined period of time. Response logic is described that initiates a response as a result of the body being classified in the posture state only after the living body has maintained the classified posture state for at least the predetermined period of time. This response may involve a change in therapy, such as neurostimulation therapy, that is delivered to the living body.

Abstract: A medical device provides stimulation therapy to a patient based on a set of therapy parameters. One or more therapy parameters may be automatically adjusted based on acceleration forces detected by a sensor, the acceleration forces being applied to the patient. In some examples, adjustments to one or more therapy parameter may be made based on an algorithm. The algorithm may be defined by acceleration and therapy parameter value pairs associated with opposite patient positions.

Abstract: Techniques relate to operating a medical device by classifying a detected posture state of a patient. This classification may be performed by comparing the detected posture state of the patient to posture state definitions available within the system. Each definition may be described in terms of a parameter (e.g., vector) indicative of a direction in three-dimensional space. The posture state definitions may be calibrated by automatically estimating values for these parameters, thereby eliminating the need for the patient to assume each posture state during the calibration process to capture actual parameter values. According to another aspect, the estimated parameter values may be updated as the patient assumes various postures during a daily routine. For instance, estimated vectors initially used to calibrate the posture state definitions may be changed over time to more closely represent posture states the patient actually assumes, and to further adapt to changes in a patient's condition.

Abstract: Techniques for posture classification of a patient in a coordinate system of a sensor. According to one aspect, a defined vector is obtained from a sensor disposed in a substantially fixed manner relative to the patient. The defined vector is described in a coordinate system of the sensor and without regard to an orientation in which the sensor is disposed in relation to the patient. A detected vector is obtained from the sensor that is described using the coordinate system of the sensor. The detected vector and the defined vector to are used to classify the posture state of the patient without regard to the orientation in which the sensor is disposed in relation to the patient. A response may be initiated by a medical device, which may include adjusting therapy delivery.

Abstract: A patient controls the delivery of therapy through volitional inputs that are detected by a biosignal within the brain. The volitional patient input may be directed towards performing a specific physical or mental activity, such as moving a muscle or performing a mathematical calculation. In one embodiment, a biosignal detection module monitors an electroencephalogram (EEG) signal from within the brain of the patient and determines whether the EEG signal includes the biosignal. In one embodiment, the biosignal detection module analyzes one or more frequency components of the EEG signal. In this manner, the patient may adjust therapy delivery by providing a volitional input that is detected by brain signals, wherein the volitional input may not require the interaction with another device, thereby eliminating the need for an external programmer to adjust therapy delivery. Example therapies include electrical stimulation, drug delivery, and delivery of sensory cues.

Abstract: Techniques for classification of a posture state of a patient using multiple posture state definitions, and for delivering therapy according to the patient's classified posture state. Detected parameter values describing a patient's posture state are compared to posture state definitions. This comparison is used to determine similarity values describing how similar the patient's posture state is to each of the posture states described by the posture state definitions. Weighting factors may be determined from the similarity values and used to weight therapy parameter values that are associated with each of the posture state definitions. The resulting weighted therapy parameter values may be used to derive a blended therapy parameter value for use in delivering therapy to the patient. The patient's posture state may be expressed in terms of a blending of the multiple posture state definitions.

Abstract: Techniques relate to operating a medical device by classifying a detected posture state of a patient. This classification may be performed by comparing the detected posture state of the patient to posture state definitions available within the system. Each definition may be described in terms of a parameter (e.g., vector) indicative of a direction in three-dimensional space. The posture state definitions may be calibrated by automatically estimating values for these parameters, thereby eliminating the need for the patient to assume each posture state during the calibration process to capture actual parameter values. According to another aspect, the estimated parameter values may be updated as the patient assumes various postures during a daily routine. For instance, estimated vectors initially used to calibrate the posture state definitions may be changed over time to more closely represent posture states the patient actually assumes, and to further adapt to changes in a patient's condition.

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.