Abstract: Apparatus and method detect a detection cluster that is associated with a neurological event, such as a seizure, of a nervous system disorder and update therapy parameters that are associated with a treatment therapy. The occurrence of the detection cluster is detected when the maximal ratio exceeds an intensity threshold. If the maximal ratio drops below the intensity threshold for a time interval that is less than a time threshold and subsequently rises above the intensity threshold, the subsequent time duration is considered as being associated with the detection cluster rather than being associated with a different detection cluster. Consequently, treatment of the nervous system disorder during the corresponding time period is in accordance with one detection cluster. Treatment therapy may be provided by providing electrical stimulation, drug infusion or a combination.

Abstract: Systems and methods for detecting and/or treating nervous system disorders, such as seizures. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Other embodiments include prediction of seizure activity based upon analysis of physiological signal levels. An embodiment of the invention monitors the quality of physiological signals, and may be able to compensate for signals of low signal quality. A further embodiment of the invention includes detection of seizure activity following the delivery of therapy.

Abstract: A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.

Abstract: A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.

Abstract: A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.

Abstract: A patient state is detected with at least one classification boundary generated by a supervised machine learning technique, such as a support vector machine. The patient state can be, for example, a patient posture state. In some examples, the patient state detection is used to at least one of control the delivery of therapy to a patient, to generate a patient notification, to initiate data recording, or to evaluate a patient condition. In addition, an evaluation metric can be determined based on a feature vector, which is determined based on characteristics of a patient parameter signal, and the classification boundary. Example evaluation metrics can be based on a distance between at least one feature vector and the classification boundary and/or a trajectory of a plurality of feature vectors relative to the classification boundary over time.

Abstract: A method and apparatus is provided for handling multiple recordings that result from events in a limited memory device. The events may include various automatic and manual triggers. The method provides a mechanism for storing different configurations of data, associated with different events.

Abstract: Systems and methods for detecting and/or treating nervous system disorders, such as seizures, are disclosed. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Other embodiments include prediction of seizure activity based upon analysis of physiological signal levels. An embodiment of the invention monitors the quality of physiological signals, and may be able to compensate for signals of low signal quality. A further embodiment of the invention includes detection of seizure activity following the delivery of therapy.

Abstract: Systems and methods for detecting and/or treating nervous system disorders, such as seizures. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Other embodiments include prediction of seizure activity based upon analysis of physiological signal levels. An embodiment of the invention monitors the quality of physiological signals, and may be able to compensate for signals of low signal quality. A further embodiment of the invention includes detection of seizure activity following the delivery of therapy.

Abstract: A method and medical device system for controlling treatment therapy delivered to treat a nervous system disorder. During start-up, the medical device system prevents the therapy device from delivering therapy to the patient for a predetermined time period after the therapy device has been activated. During operation, before the therapy device is programmed, the system performs a check on the programming information to ensure that the programming information will result in treatment therapy being within of an acceptable range of one or more therapy parameters.

Abstract: Brain signals may be monitored at different locations of a mood circuit in order to determine a mood state of the patient. A relationship (e.g., a ratio) between frequency band characteristics of the monitored brain signals may be indicative of a particular mood state. In some examples, therapy parameter values that define the therapy delivered to the patient may be selected to maintain a target relationship (e.g., a target ratio) between the frequency band characteristics of the brain signals monitored within the mood circuit. In addition, in some examples, therapy delivery to the patient may be controlled based on the frequency band characteristics of brain signals sensed at different portions of the mood circuit.

Abstract: A portable seating device (100?) is disclosed that has a seat (104?) with one or more internal storage compartments (224/230). The seat (104?) includes an upper seat section (108?) that may be pivoted away from a platform (112?) to access one or more storage compartments (224) on a lower surface of the upper seat section (108?) and to access one or more storage compartments (230) on an upper surface of the platform (112?). The platform (112?) is rotatably interconnected with a base (152). A backrest assembly (127) is appropriately interconnected with the seat (104?), and a stand (172) is detachably interconnected with the base (152) to accommodate multiple configurations for the portable seating device (100?). Preferably, the backrest assembly (127) is movable between stowed and deployed positions, and includes one or more adjustment features when in its deployed position.

Abstract: A method and apparatus is provided for handling multiple loop recordings that result from events in a limited memory implantable device. The events may include various automatic and manual triggers. The method provides a mechanism for deciding the amount of information to store associated with each overlapping loop recording.

Abstract: Systems and methods for detecting and/or treating nervous system disorders, such as seizures, are disclosed. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Other embodiments include prediction of seizure activity based upon analysis of physiological signal levels. An embodiment of the invention monitors the quality of physiological signals, and may be able to compensate for signals of low signal quality. A further embodiment of the invention includes detection of seizure activity following the delivery of therapy.

Abstract: Systems and methods for detecting and/or treating nervous system disorders, such as seizures, are disclosed. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Other embodiments include prediction of seizure activity based upon analysis of physiological signal levels. An embodiment of the invention monitors the quality of physiological signals, and may be able to compensate for signals of low signal quality. A further embodiment of the invention includes detection of seizure activity following the delivery of therapy.

Abstract: Systems and methods for detecting and/or treating nervous system disorders, such as seizures, are disclosed. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Further embodiments of the invention include preparing for the delivery of therapy by warming up therapy delivery components prior to the expected delivery of therapy.

Abstract: Systems and methods for detecting and/or treating nervous system disorders, such as seizures, are disclosed. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Other embodiments include prediction of seizure activity based upon analysis of physiological signal levels. An embodiment of the invention monitors the quality of physiological signals, and may be able to compensate for signals of low signal quality. A further embodiment of the invention includes detection of seizure activity following the delivery of therapy.

Abstract: Systems and methods for detecting and/or treating nervous system disorders, such as seizures, are disclosed. Certain embodiments of the invention relate generally to implantable medical devices (IMDs) adapted to detect and treat nervous system disorders in patients with an IMD. Certain embodiments of the invention include detection of seizures based upon comparisons of long-term and short-term representations of physiological signals. Other embodiments include prediction of seizure activity based upon analysis of physiological signal levels. An embodiment of the invention monitors the quality of physiological signals, and may be able to compensate for signals of low signal quality. A further embodiment of the invention includes detection of seizure activity following the delivery of therapy.

Abstract: Apparatus and method support a repetitive administration of a therapeutic treatment through a delivery unit during a neurological event, such as a seizure, of a nervous system disorder. The medical device system monitors signal data from a sensor and through an amplifier. A neurological event is detected by a medical device system using a detection algorithm. Consequently, a electrical stimulation pulse is applied to an electrode. During the delivery of the electrical stimulation pulse, the medical device system blanks signal data that is associated with the electrode. Subsequently, additional blanking is providing during a time interval in which signal artifacts may occur and the amplifier is stabilizing. The medical device system then collects signal data for the detection algorithm to stabilize. Subsequently, the medical device system collects additional signal data in order to obtain statistically meaningful data.

Abstract: Apparatus and method detect a detection cluster that is associated with a neurological event, such as a seizure, of a nervous system disorder and update therapy parameters that are associated with a treatment therapy. The occurrence of the detection cluster is detected when the maximal ratio exceeds an intensity threshold. If the maximal ratio drops below the intensity threshold for a time interval that is less than a time threshold and subsequently rises above the intensity threshold, the subsequent time duration is considered as being associated with the detection cluster rather than being associated with a different detection cluster. Consequently, treatment of the nervous system disorder during the corresponding time period is in accordance with one detection cluster. Treatment therapy may be provided by providing electrical stimulation, drug infusion or a combination.