Abstract:
A deep learning model and dimensionality reduction are applied to each of a plurality of records of physiological information to derive a plurality of feature vectors. A similarities algorithm is applied to the plurality of feature vectors to form a plurality of clusters, each including a set of feature vectors. An output comprising information that enables a display of one or more of the plurality of clusters is provided, and a mechanism for selecting at least one feature vector within a selected cluster of the plurality of clusters is enabled. Upon selection of a feature vector, an output comprising information that enables a display of the record of physiological information corresponding to the selected feature vector is provided, and a mechanism for assigning a label to the displayed record is enabled. The assigned label is then automatically assigned to the records corresponding to the remaining feature vectors in the selected cluster.
Abstract:
Information relevant to making clinical decisions for a patient is identified based on electrical activity records of the patient's brain and electrical activity records of other patients' brains. A deep learning algorithm is applied to an electrical activity record of the patient, i.e., an input record, and to a set of electrical activity records of other patients, i.e., a set of search records, to obtain an input feature vector of the patient and a set of search feature vectors, each including features extracted by the deep learning algorithm. A similarities algorithm is applied to the input feature vector and the set of search feature vectors to identify a subset of search records most like the input record. Clinical information associated with one or more search records in the identified subset of search records is extracted from a database and used to make decisions regarding the patient's neuromodulation therapies.
Abstract:
A sensor of an implantable medical device senses electrical activity of the brain. A data analyzer of the device monitors an electrographic signal corresponding to the electrical activity of the sensed brain signal, and processes the brain signal to obtain a measure of phase-amplitude coupling. For a selected portion of the electrographic signal, the data analyzer detects first features and second features of the electrographic signal. The first features represent oscillations in a low frequency range, while the second features represent oscillations in a frequency range higher than the low frequency range. For example, the low frequency range may correspond to theta frequency and the higher frequency range may correspond to gamma frequency. The data analyzer determines a measure of phase-amplitude coupling between oscillations in the low frequency range and oscillations in the higher frequency range based on occurrences of second features which coincide with first features.
Abstract:
An implantable neurostimulator system adapted to provide therapy for various neurological disorders is capable of varying therapy delivery strategies based on the context, physiological or otherwise, into which the therapy is to be delivered. Responsive and scheduled therapies can be varied depending on various sensor measurements, calculations, inferences, and device states (including elapsed times and times of day) to deliver an appropriate course of therapy under the circumstances.
Abstract:
A medical lead with at least a distal portion thereof implantable in the brain of a patient is described, together with methods and systems for using the lead. The lead is provided with at least two sensing modalities (e.g., two or more sensing modalities for measurements of field potential measurements, neuronal single unit activity, neuronal multi unit activity, optical blood volume, optical blood oxygenation, voltammetry and rheoencephalography). Acquisition of measurements and the lead components and other components for accomplishing a measurement in each modality are also described as are various applications for the multimodal brain sensing lead.
Abstract:
An implantable neurostimulator system for treating movement disorders includes a sensor, a detection subsystem capable of identifying episodes of a movement disorder by analyzing a signal received from the sensor, and a therapy subsystem capable of supplying therapeutic electrical stimulation to treat the movement disorder. The system treats movement disorders by detecting physiological conditions characteristic of an episode of symptoms of the movement disorder and selectively initiating therapy when such conditions are detected.
Abstract:
An implantable neurostimulator system adapted to provide therapy for various neurological disorders is capable of varying therapy delivery strategies based on the context, physiological or otherwise, into which the therapy is to be delivered. Responsive and scheduled therapies can be varied depending on various sensor measurements, calculations, inferences, and device states (including elapsed times and times of day) to deliver an appropriate course of therapy under the circumstances.
Abstract:
A deep learning model and dimensionality reduction are applied to each of a plurality of records of physiological information to derive a plurality of feature vectors. A similarities algorithm is applied to the plurality of feature vectors to form a plurality of clusters, each including a set of feature vectors. An output comprising information that enables a display of one or more of the plurality of clusters is provided, and a mechanism for selecting at least one feature vector within a selected cluster of the plurality of clusters is enabled. Upon selection of a feature vector, an output comprising information that enables a display of the record of physiological information corresponding to the selected feature vector is provided, and a mechanism for assigning a label to the displayed record is enabled. The assigned label is then automatically assigned to the records corresponding to the remaining feature vectors in the selected cluster.
Abstract:
A medical lead with at least a distal portion thereof implantable in the brain of a patient is described, together with methods and systems for using the lead. The lead is provided with at least two sensing modalities (e.g., two or more sensing modalities for measurements of field potential measurements, neuronal single unit activity, neuronal multi unit activity, optical blood volume, optical blood oxygenation, voltammetry and rheoencephalography). Acquisition of measurements and the lead components and other components for accomplishing a measurement in each modality are also described as are various applications for the multimodal brain sensing lead.
Abstract:
A method of assessing electrical activity of a brain includes, for each of a plurality of electrical-activity records of the brain, applying a machine-learned ESC model to the record to classify the record as one of a seizure record or a non-seizure record, wherein each of record is sensed by a corresponding one of a plurality of sensing channels of an implanted medical device; for each seizure record in a set of seizure records, applying the machine-learned ESC model to the seizure record to classify the seizure record as one of a local-seizure record or a spread-seizure record, wherein the seizure record comprises a first seizure record captured by a first sensing channel and a second seizure record captured by a second sensing channel; and for each spread-seizure record in a set of spread-seizure records, applying a machine-learned SSC model to the spread-seizure record to classify the spread-seizure record as a type of seizure spread pattern.