Flint Hills Scientific, LLC

Other

Medtronic, Inc.

Cyberonics, Inc.

Globalfoundries Inc.

University of Kansas

Uchicago Argonne, LLC

Plint Hills Scientific LLC

Flunt Hills Scientific, LLC

Ivan Osorio

Mark G Frei

Naresh C Bhavaraju

Alexey Lyubushin

Nina M Graves

Didier Sornette

Jonathon E Giftakis

David L Carlson

Mark Turner Rise

Scott F Schaffner

Jonathan C Werder

Alexei V Nikitin

Thomas E Peters

John Craig Colborn

Josef Dorfmeister

David Lerner

John Ralston

Apostolos C Raptis

Timothy L Scott

Nachappa Gopalsami

Randy M Jensen

Ruslan L Davidchack

Stanislav Kulikov

D Sornette

John C Colborn

A method for identifying changes in an epilepsy patient's disease state, comprising: receiving at least one body data stream; determining at least one body index from the at least one body data stream; detecting a plurality of seizure events from the at least one body index; determining at least one seizure metric value for each seizure event; performing a first classification analysis of the plurality of seizure events from the at least one seizure metric value; detecting at least one additional seizure event from the at least one determined index; determining at least one seizure metric value for each additional seizure event, performing a second classification analysis of the plurality of seizure events and the at least one additional seizure event based upon the at least one seizure metric value; comparing the results of the first classification analysis and the second classification analysis; and performing a further action.

Detecting, assessing and managing epilepsy using a multi-variate, metric based classification analysis

We report a method of determining an occurrence of an epileptic convulsive seizure in a patient, comprising: receiving body data from a patient during a first time period, determining a work level relating to said first time period at least based partially upon said body data; determining whether said work level exceeds an extreme work level threshold; performing a responsive action, in response to a determination that said work level exceeds said extreme work level threshold. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

Seizure detection based on work level excursion

Disclosed are methods and systems for treating epilepsy by stimulating a main trunk of a vagus nerve, or a left vagus nerve, when the patient has had no seizure or a seizure that is not characterized by cardiac changes such as an increase in heart rate, and stimulating a cardiac branch of a vagus nerve, or a right vagus nerve, when the patient has had a seizure characterized by cardiac changes such as a heart rate increase.

Contingent cardio-protection for epilepsy patients

We report a method of detecting a pathological body state of a patient, comprising receiving a body signal of the patient; determining a BDV from said body signal; determining an activity level of said patient; determining a value range for said BDV for said patient, based at least in part on said activity level; comparing said BDV to said value range; and detecting a pathological state when said BDV is outside said value range. We also report a medical device system configured to implement the method. We also report a non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

Pathological state detection using dynamically determined body data variability range values

Methods, systems, and apparatus for assessing a state of an epilepsy disease or a comorbidity thereof are provided. The methods comprise receiving at least one autonomic index, neurologic index, stress marker index, psychiatric index, endocrine index, adverse effect of therapy index, physical fitness index, or quality of life index of a patient; comparing the at least one index to at least one reference value; and assessing a state of an epilepsy disease or a body system of the patient based on the comparison. A computer readable program storage device encoded with instructions that, when executed by a computer, perform the method described above is also provided. A medical device system capable of implementing the method described above is also provided.

Systems approach to disease state and health assessment

In one embodiment, the present disclosure relates to an implantable sensor system for sensing biological signal of a patient comprising: an anchor for coupling the sensor to at least one patient skull base structure; a first sensor coupled to the anchor for sensing, at a first location proximate to the at least one patient skull base structure, at least one biological signal of a patient; and a transmitter coupled to the sensor and capable of transmitting data from the sensor to an external device.

Sensor systems implantable at bones of the face or the skull base

Methods, systems, and apparatus for determining probabilistic measures of seizure activity (PMSA) values based on a plurality of seizure detection algorithms and/or body signals used as inputs by the seizure detection algorithms. Use of the PMSA values to detect seizure activity based on a consensus of the algorithms and/or body signals, and/or warn, log, administer a therapy, or assess the efficacy of a therapy.

Apparatus and systems for event detection using probabilistic measures

Methods, system and apparatus for identifying an extreme epileptic state/event in a patient are provided. In one aspect, at least two seizure events are identified. At least one inter-seizure interval (ISI) value related to the at least two seizure events are determined. The ISI value is compared to at least one reference value. An occurrence of an extreme seizure event is determined based upon the comparison of the determined ISI value to the at least one reference value. In another aspect, a first seizure event is detected. At least one body index affected by the first seizure event is determined. A second seizure event is detected. An occurrence of an extreme seizure event is determined based at least in part in response to a determination that the second seizure event occurred prior to the body index value returning to a reference value.

Detecting, assessing and managing extreme seizure events

Methods, systems, and apparatus for detecting and/or validating a detection of a state change by matching the shape of one or more of an cardiac data series, a heart rate variability data series, or at least a portion of a heart beat complex, derived from cardiac data, to an appropriate template.

Detecting or validating a detection of a state change from a template of heart rate derivative shape or heart beat wave complex

A method, comprising detecting, in at least a first brain region of a patient, an electrical activity relating to an epileptic activity; determining a first regularity index of said electrical activity; and applying at least one first electrical stimulation to at least one neural target of said patient for treating said epileptic event, in response to said first regularity index being within a first range. A non-transitive, computer-readable storage device for storing instructions that, when executed by a processor, perform the method. A medical device system suitable for use in the method.

System and apparatus for increasing regularity and/or phase-locking of neuronal activity relating to an epileptic event

A method, comprising receiving a time series of patient body signal, determining first and second sliding time windows for the time series; applying an autoregression algorithm, comprising: applying an autoregression analysis to each of the first and second windows, yielding autoregression coefficients and a residual variance for each window; estimating a parameter vector for each window based on the autoregression coefficients and residual variances; and determining a difference between the parameter vectors; and determining seizure onset and seizure termination based on the difference between the parameter vectors. A non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

Seizure detection methods, apparatus, and systems using an autoregression algorithm

Growing community of inventors

Leawood, KS, United States of America

Ivan Osorio