Medtronic, Inc.

Covidien LP

Eric J Panken

Jonathan C Werder

Jonathon E Giftakis

Timothy J Denison

Scott R Stanslaski

Nina M Graves

David L Carlson

Christopher L Pulliam

Jadin C Jackson

Touby Austin Drew

Dennis M Skelton

William J Combs

Steven M Goetz

Jon P Davis

Gregory F Molnar

John Wainwright

Keith Alan Miesel

Randal C Schulhauser

William Frederick Kaemmerer

Duane L Bourget

Brian Bruce Lee

Rene A Molina

Abbey Beuning Holt Becker

Yizi Xiao

Weimin Sun

Michelle A Case

Paul G Krause

Avram Scheiner

James D Reinke

Rajeev Madhukar Sahasrabudhe

Timothy R Abraham

Shyam Gokaldas

Wende L Dewing

Lynn A Davenport

Elizabeth A Fehrmann

Eric M Christensen

Carl D Wahlstrand

Eduardo N Warman

Steven Streatfield Gill

David A Anderson

Mark S Lent

Richard J O'Brien

Andrew H Houchins

Patrick W Kinzie

Siddharth Dani

Robert S Raike

Thomas Adamski

Christopher M Arnett

Benjamin P Isaacson

Shahram Malekkhosravi

Thomas L Chouinard

Ashutosh Chaturvedi

Lukas Valine

Jeffrey Herron

Evan Schnell

Alicia Weller Thompson

Ilan D Gordon

Karan Chitkara

Caleb C Zarns

Ekaterina M Ippolito

Amanda D Gaudreau

Hijaz M Haris

Paula Andrea Elma Dassbach Green

Ankush Thakur

Brady N Fetting

Rucha Gokul G Samant

Alejo Chavez Gaxiola

Aaron Gilletti

Julia Slopsema

Brandon Zingsheim

Andrew J Ries

Yong Kyun Cho

Saul E Greenhut

Karen J Kleckner

Can Cinbis

Mark Turner Rise

John D Wahlstrand

Mark R Boone

Jianping Wu

John J Grevious

David J Miller

John E Nicholson

James Strom

Michael B Shelton

Christopher Poletto

Timothy Denison

Richard S Schallhorn

Heather Diane Orser

Mandla Thokozani Shongwe

Nathalie Virag

Kevin K Tidemand

Charles G Yerich

Kevin L Bright

Thomas I Ceballos

Michael R Kane

Gerard J Hill

Kevin M Johnson

Susan Heilman Kilbane

Susan E Heilman Kilbane

Chark Stanford

Devices, systems, and techniques for identifying electrodes closest to a target region of tissue are described. In one example, a device includes sensing circuitry configured to sense electrical signals from a plurality of electrode combinations. Processing circuitry identifies a first electrode combination of a first subset of electrode combinations. Each electrode combination of the first subset of electrode combination includes electrodes located at different axial positions along a length of the medical lead. The processing circuitry identifies a second electrode combination of a second subset of electrode combinations. Each electrode combination of the second subset of electrode combinations includes electrodes located at a same axial position and different circumferential positions around a perimeter of the medical lead. The processing circuitry then determines a third electrode combination and controls delivery of electrical stimulation via the third electrode combination.

Directional sensing for programming guidance

A system for detecting strokes includes a sensor device configured to obtain physiological data from a patient, for example brain activity data. The sensor device can include electrodes configured to be disposed at the back of the patient's neck or base of the skull. The electrodes can detect electrical signals corresponding to brain activity in the P3, Pz, and/or P4 brain regions or other brain regions. A computing device communicatively coupled to the sensor device is configured to receive the physiological data and analyze it to indicate whether the patient has suffered a stroke.

Systems and methods for detecting strokes

In some examples, a device includes at least three electrodes a first pair of electrodes and a second pair of electrodes. The device also includes circuitry configured to generate a first cardiac signal based on a first differential signal received across the first pair, generate a first brain signal based on the first differential signal received across the first pair, generate a second cardiac signal based on a second differential signal received across the second pair, and generate a second brain signal based on the second differential signal received across the second pair. The circuitry is also configured to output a composite cardiac signal based on the first cardiac signal and the second cardiac signal and to output a composite brain signal based on the first brain signal and the second brain signal.

Determining composite signals from at least three electrodes

A system comprises a sensor device and processing circuitry. The sensor device comprises a housing configured to be disposed above shoulders of a patient, a plurality of electrodes on the housing, a motion sensor, and sensing circuitry configured to sense a brain electrical signal and a cardiac electrical signal via the electrodes, and a motion signal via the motion sensor. The processing circuitry is configured to determine values over time of one or more parameters from the brain electrical signal, determine values over time of one or more parameters from the cardiac electrical signal, and generate at least one of a detection, prediction, or a classification a condition of the patient based on the values and the motion signal.

Detection of patient conditions using signals sensed on or near the head

Devices, systems, and techniques are described for identifying stimulation parameter values based on electrical stimulation that induces dyskinesia for the patient. For example, a method may include controlling, by processing circuitry, a medical device to deliver electrical stimulation to a portion of a brain of a patient, receiving, by the processing circuitry, information representative of an electrical signal sensed from the brain after delivery of the electrical stimulation, determining, by the processing circuitry and from the information representative of the electrical signal, a peak in a spectral power of the electrical signal at a second frequency lower than a first frequency of the electrical stimulation, and responsive to determining the peak in the spectral power of the electrical signal at the second frequency, performing, by the processing circuitry, an action.

Stimulation induced neural response for parameter selection

Devices, systems, and techniques are disclosed for managing electrical stimulation therapy and/or sensing of physiological signals such as brain signals. For example, a system may assist a clinician in identifying one or more electrode combinations for sensing a brain signal. In another example, a user interface may display brain signal information and values of a stimulation parameter at least partially defining electrical stimulation delivered to a patient when the brain signal information was sensed.

Brain stimulation and sensing

A system for providing stimulation to a patient includes one or more processors implemented in circuitry and one or more accelerometers configured to generate one or more accelerometer signals. The one or more processors are configured to determine accelerometer information for a medical device associated with the patient based on the one or more accelerometer signals and convert the accelerometer information into frequency domain coefficients. The one or more processors are further configured to determine an activity level for the patient based on the frequency domain coefficients and determine one or more stimulation parameters based on the activity level. The one or more processors are further configured to output electrical stimulation to the patient based on the one or more stimulation parameters.

Medical device using spectral activity processing

Techniques related to classifying a posture state of a living body are disclosed. One aspect relates to sensing at least one signal indicative of a posture state of a living body. Posture state detection logic classifies the living body as being in a posture state based on the at least one signal, wherein this classification may take into account at least one of posture and activity state of the living body. The posture state detection logic further determines whether the living body is classified in the posture state for at least a predetermined period of time. Response logic is described that initiates a response as a result of the body being classified in the posture state only after the living body has maintained the classified posture state for at least the predetermined period of time. This response may involve a change in therapy, such as neurostimulation therapy, that is delivered to the living body.

Posture state responsive therapy delivery using dwell times

Techniques are disclosed for defining a homeostatic window for controlling delivery of electrical stimulation therapy to a patient. In one example, a method includes generating and delivering electrical stimulation therapy to tissue of a patient via electrodes. Further, the method includes adjusting a level of a parameter of the electrical stimulation therapy such that a signal of the patient is not less than a lower bound and not greater than an upper bound. The lower bound is determined to be the magnitude of the signal while receiving electrical stimulation therapy sufficient to reduce one or more symptoms of a disease while the patient was receiving medication for reduction of the one or more symptoms. Further, the upper bound is determined to be the magnitude of the signal while receiving electrical stimulation therapy sufficient to reduce the one or more symptoms when the patient was not receiving the medication.

Adaptive deep brain stimulation using homeostatic window

In some examples, a processor of a system evaluates a therapy program based on a score determined based on a volume of tissue expected to be activated ('VTA') by therapy delivery according to the therapy program. The score may be determined using an efficacy map comprising a plurality of voxels that are each assigned a value. In some examples, the efficacy map is selected from a plurality of stored efficacy maps based on a patient condition, one or more patient symptoms, or both the patient condition and one or more patient symptoms. In addition, in some examples, voxels of the efficacy map are assigned respective values that are associated with a clinical rating scale.

Therapy program selection for electrical stimulation therapy based on a volume of tissue activation

A medical device may receive sensor data from sensing sources, and determine confidence levels for sensor data received from each of the plurality of sensing sources. Each of the confidence levels of the sensor data from each of the sensing sources is a measure of accuracy of the sensor data received from respective sensing sources. The medical device may also determine one or more therapy parameter values based on the determined confidence levels, and cause delivery of therapy based on the determined one or more therapy parameter values.

Closed-loop stimulation therapy in event of loss of sensor data

Devices, systems, and techniques are configured for identifying stimulation parameter values based on electrical stimulation that induces dyskinesia for the patient. For example, a method may include controlling, by processing circuitry, a medical device to deliver electrical stimulation to a portion of a brain of a patient, receiving, by the processing circuitry, information representative of an electrical signal sensed from the brain after delivery of the electrical stimulation, determining, by the processing circuitry and from the information representative of the electrical signal, a peak in a spectral power of the electrical signal at a second frequency lower than a first frequency of the electrical stimulation, and responsive to determining the peak in the spectral power of the electrical signal at the second frequency, performing, by the processing circuitry, an action.

Stimulation induced neural response for detection of lead movement

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Edina, MN, United States of America

Eric J Panken