Abstract: A method includes determining sleep cycle information related to a sleep cycle of a patient based on body parameter data. The method also includes adjusting a cranial nerve stimulation parameter based on the sleep cycle information.

Abstract: A method includes determining sleep cycle information related to a sleep cycle of a patient based on body parameter data. The method also includes adjusting a cranial nerve stimulation parameter based on the sleep cycle information.

Abstract: A method of detecting a subject's physiological status, seizure state, or seizure type with limb-based accelerometers identifying a favored accelerometer or limb that provides a better sensitivity or false positive rate as compared to other accelerometers or limbs. The detection method may include determining whether the seizure conforms with an anticipated seizure type, and determining whether to base a seizure detection on data from the favored or un-favored accelerometers or limbs. The method may also include applying a weighing factor to base the detection on data obtained primarily from the favored accelerometer or limb or to not rely on data obtained primarily from un-favored accelerometers or limbs. The method may also include comparing data obtained from a favored accelerometer or limb with data obtained from other accelerometers or limbs or obtained from detection devices using ECG or EEG. An alert may be generated based on the detection of a seizure.

Abstract: A motion monitoring system and a method of obtaining and analyzing motion data that uses an accelerometer or an image capture device to acquire subject motion data. The motion data may be obtained from an accelerometer applied to a chest of the subject, or from an image capture device configured to view the subject during the detected motion. The motion data is analyzed to distinguish between a seizure type of motion and a non-seizure type of motion, with the subject motion characterized by at least one of, and combinations of, motion amplitude or magnitude, motion period or frequency, motion bandwidth, subject position, and subject change in position over a time period of the detected motion. The system and method further includes the generation of an output in response to an identification of the seizure and non-seizure types of motion.

Abstract: This disclosure relates to devices, systems, and methods for validating locational data for a monitoring device. The external monitoring device located on a patient may include one or more processors, one or more memory devices, one or more power devices, one or more heart rate detection devices, and one or more heart sound detection devices. Further, the method may include determining a plurality of status of an external monitoring device located on a patient via one or more processors based on obtained heart rate data and obtained heart sound data. The external monitoring device state may be generated via a validation module based on the heart rate data and the heart sound data.

Abstract: A method includes determining a measurement indicative of a synchrony between one or more regions of a patient's autonomic nervous system (e.g., the NTS) and one or more regions of the patient's central nervous system (e.g., the thalamus, the cortex) based on body parameter data. The method also includes adjusting a cranial nerve stimulation parameter based on the measurement.

Abstract: A method includes determining sleep cycle information related to a sleep cycle of a patient based on body parameter data. The method also includes adjusting a cranial nerve stimulation parameter based on the sleep cycle information.

Abstract: Methods, systems, and apparatuses for detecting seizure events are disclosed, having at least one accelerometer to be positioned on a patient and configured to collect acceleration data and a processor in communication with the at least one accelerometer and configured to receive acceleration data from the at least one accelerometer. The processor may apply at least one non-linear operator to the acceleration data to determine whether the acceleration data indicates an event, such application including calculation of a non-linear energy of the acceleration data and performance of at least one secondary analysis to determine whether the event is a seizure.

Abstract: Methods, systems, and apparatuses for detecting seizure events are disclosed, including a system for identification of an increased risk of a severe neurological event. The system may include an electroencephalogram (“EEG”) monitoring unit configured to collect EEG data from the patient during at least a postictal phase of one or more seizures and a processing unit configured to receive the EEG data from the EEG monitoring unit. The processing unit is configured to detect postictal EEG suppression from the EEG data and to identify the increased risk of the severe neurological event based on the detected postictal EEG suppression. Other embodiments are described and claimed.

Abstract: For analyzing a multi-component system, a method acquires a plurality of signals, each having a different spatial location of the multi-component system, and generates dynamic profiles for each of the plurality of signals. Each of the plurality of dynamic profiles reflects dynamic characteristics of the corresponding signal in accordance with each one of a plurality of dynamic measures. The method selects pairs of dynamic profiles from the acquired dynamic profiles based on a predetermined level of synchronization and generates a statistical measure for each of the selected plurality of pairs of dynamic profiles. The method characterizes state dynamics of the multi-component system as a function of at least one of the generated statistical measures, and generates a signal indicative of the characterized state dynamics of the multi-component system.