Abstract: Devices, systems, and techniques are described for detecting stroke or seizure with a compact system. For example, a system includes a memory, a plurality of electrodes, and sensing circuitry configured to sense, via at least two electrodes of the plurality of electrodes, electrical signals from a patient, and generate, based on the electrical signals, physiological information. The system may also include processing circuitry configured to receive, from the sensing circuitry, the physiological information, determine, based on the physiological information, a seizure metric indicative of a seizure status of the patient and a stroke metric indicative of a stroke status of the patient, and store the seizure metric and the stroke metric in the memory. A housing may carry the plurality of electrodes and contain both of the sensing circuitry and the processing circuitry.

Abstract: In some examples, a device includes at least three electrodes a first pair of electrodes and a second pair of electrodes. The device also includes circuitry configured to generate a first cardiac signal based on a first differential signal received across the first pair, generate a first brain signal based on the first differential signal received across the first pair, generate a second cardiac signal based on a second differential signal received across the second pair, and generate a second brain signal based on the second differential signal received across the second pair. The circuitry is also configured to output a composite cardiac signal based on the first cardiac signal and the second cardiac signal and to output a composite brain signal based on the first brain signal and the second brain signal.

Abstract: A system comprises a sensor device and processing circuitry. The sensor device comprises a housing configured to be disposed above shoulders of a patient, a plurality of electrodes on the housing, a motion sensor, and sensing circuitry configured to sense a brain electrical signal and a cardiac electrical signal via the electrodes, and a motion signal via the motion sensor. The processing circuitry is configured to determine values over time of one or more parameters from the brain electrical signal, determine values over time of one or more parameters from the cardiac electrical signal, and generate at least one of a detection, prediction, or a classification a condition of the patient based on the values and the motion signal.

Abstract: Devices, systems, and techniques are described for identifying stimulation parameter values based on electrical stimulation that induces dyskinesia for the patient. For example, a method may include controlling, by processing circuitry, a medical device to deliver electrical stimulation to a portion of a brain of a patient, receiving, by the processing circuitry, information representative of an electrical signal sensed from the brain after delivery of the electrical stimulation, determining, by the processing circuitry and from the information representative of the electrical signal, a peak in a spectral power of the electrical signal at a second frequency lower than a first frequency of the electrical stimulation, and responsive to determining the peak in the spectral power of the electrical signal at the second frequency, performing, by the processing circuitry, an action.

Abstract: Devices, systems, and techniques are described for identifying stimulation parameter values based on electrical stimulation that induces dyskinesia for the patient. For example, a method may include controlling, by processing circuitry, a medical device to deliver electrical stimulation to a portion of a brain of a patient, receiving, by the processing circuitry, information representative of an electrical signal sensed from the brain after delivery of the electrical stimulation, determining, by the processing circuitry and from the information representative of the electrical signal, a peak in a spectral power of the electrical signal at a second frequency lower than a first frequency of the electrical stimulation, and responsive to determining the peak in the spectral power of the electrical signal at the second frequency, performing, by the processing circuitry, an action.

Abstract: Techniques are described to determine a location of at least one oscillatory signal source in a patient. Processing circuitry may determine expected electrical signal levels based on a hypothetical location of the at least one oscillatory signal source. Processing circuitry may determine the electrical signal levels and determine an error value based on the expected electrical signal levels and the determined electrical signal levels. Processing circuitry may adjust the hypothetical location of the at least one oscillatory signal source until the error value is less than or equal to a threshold value, including the example where the error value is minimized.

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: A system for detecting strokes includes a sensor device configured to obtain physiological data from a patient, for example brain activity data. The sensor device can include electrodes configured to be disposed at the back of the patient's neck or base of the skull. The electrodes can detect electrical signals corresponding to brain activity in the P3, Pz, and/or P4 brain regions or other brain regions. A computing device communicatively coupled to the sensor device is configured to receive the physiological data and analyze it to indicate whether the patient has suffered a stroke.

Abstract: A system for detecting strokes includes a sensor device configured to obtain physiological data from a patient, for example brain activity data. The sensor device can include electrodes configured to be disposed at the back of the patient's neck or base of the skull. The electrodes can detect electrical signals corresponding to brain activity in the P3, Pz, and/or P4 brain regions or other brain regions. A computing device communicatively coupled to the sensor device is configured to receive the physiological data and analyze it to indicate whether the patient has suffered a stroke.

Abstract: Devices, systems, and techniques are disclosed for managing electrical stimulation therapy and/or sensing of physiological signals such as brain signals. For example, a system may assist a clinician in identifying one or more electrode combinations for sensing a brain signal. In another example, a user interface may display brain signal information and values of a stimulation parameter at least partially defining electrical stimulation delivered to a patient when the brain signal information was sensed.

Abstract: Devices, systems, and techniques are disclosed for managing electrical stimulation therapy and/or sensing of physiological signals such as brain signals. For example, a system may assist a clinician in identifying one or more electrode combinations for sensing a brain signal. In another example, a user interface may display brain signal information and values of a stimulation parameter at least partially defining electrical stimulation delivered to a patient when the brain signal information was sensed.

Abstract: Techniques are described determining electrodes that are proximate or distal to location of an oscillatory signal source in a patient based on current source densities (CSDs). Processing circuitry may determine, for one or more electrodes of a plurality of electrodes, respective time-varying measurements of CSDs, aggregate, for the one or more electrodes of the plurality electrodes, the respective time-varying measurements of the CSDs to generate respective average level values for the one or more electrodes of the plurality of electrodes, determine, for one or more electrodes of the plurality of electrodes, respective phase-magnitude representations of the time-varying measurements of the CSDs. The respective phase-magnitude representations are indicative of respective magnitudes and phases of a particular frequency component of respective time-varying measurements of the CSDs.

Abstract: Techniques are disclosed for using probabilistic entropy to select electrodes with fewer artifacts for controlling adaptive electrical neurostimulation. In one example, a plurality of electrodes sense bioelectrical signals of a brain of a patient. Processing circuitry determines, for each bioelectrical signal sensed at a respective electrode of the plurality of electrodes, a probabilistic entropy value of the bioelectrical signal. The processing circuitry compares each of the respective probabilistic entropy values of the bioelectrical signal to respective entropy threshold values and selects, based on the comparisons, a subset of electrodes of the plurality of electrodes. The processing circuitry controls, based on the bioelectrical signals sensed via respective electrodes of the subset of electrodes and excluding the bioelectrical signals of the plurality of bioelectrical signals sensed via respective electrodes not in the subset of electrodes, delivery of electrical stimulation therapy to the patient.

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: 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: 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 are described determining electrodes that are proximate or distal to location of an oscillatory signal source in a patient based on current source densities (CSDs). Processing circuitry may determine, for one or more electrodes of a plurality of electrodes, respective time-varying measurements of CSDs, aggregate, for the one or more electrodes of the plurality electrodes, the respective time-varying measurements of the CSDs to generate respective average level values for the one or more electrodes of the plurality of electrodes, determine, for one or more electrodes of the plurality of electrodes, respective phase-magnitude representations of the time-varying measurements of the CSDs. The respective phase-magnitude representations are indicative of respective magnitudes and phases of a particular frequency component of respective time-varying measurements of the CSDs.

Abstract: Techniques are described to determine a location of at least one oscillatory signal source in a patient. Processing circuitry may determine expected electrical signal levels based on a hypothetical location of the at least one oscillatory signal source. Processing circuitry may determine the electrical signal levels and determine an error value based on the expected electrical signal levels and the determined electrical signal levels. Processing circuitry may adjust the hypothetical location of the at least one oscillatory signal source until the error value is less than or equal to a threshold value, including the example where the error value is minimized.

Abstract: Techniques are disclosed for using probabilistic entropy to select electrodes with fewer artifacts for controlling adaptive electrical neurostimulation. In one example, a plurality of electrodes sense bioelectrical signals of a brain of a patient. Processing circuitry determines, for each bioelectrical signal sensed at a respective electrode of the plurality of electrodes, a probabilistic entropy value of the bioelectrical signal. The processing circuitry compares each of the respective probabilistic entropy values of the bioelectrical signal to respective entropy threshold values and selects, based on the comparisons, a subset of electrodes of the plurality of electrodes. The processing circuitry controls, based on the bioelectrical signals sensed via respective electrodes of the subset of electrodes and excluding the bioelectrical signals of the plurality of bioelectrical signals sensed via respective electrodes not in the subset of electrodes, delivery of electrical stimulation therapy to the patient.