LivaNova USA, Inc.

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 or 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.

Methods for detecting neurological and clinical manifestations of a seizure are provided. Systems are described including a monitoring device having a communication assembly for receiving neurological data transmitted external to a patient from a transmitter implanted in a patient; a processor that processes the neurological data to estimate the patient's brain state; and an assembly for automatically recording clinical manifestation data in response to a brain state estimate by the processor.

A method of detecting a seizure status of a subject includes collecting first data from a first accelerometer positioned on a first limb of the subject and a second data from a second accelerometer positioned on a torso of the subject, determining that one of the first accelerometer or the second accelerometer provides a better indication (e.g., greater level of sensitivity or a lesser level of a false positive rate) by comparing the first data and the second data, detecting an indication of the seizure status of the subject using at least one of the first data and the second data, and generating an alert in response to the detection of the indication of the seizure status of the subject.

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.