George J Vachtsevanos

US Patent:

6594524, Jul 15, 2003

Filed:

Dec 12, 2000

Appl. No.:

09/735364

Inventors:

Rosana Esteller - Marietta GA
Javier Ramón Echauz - Atlanta GA
Brian Litt - Merion PA
George John Vachtsevanos - Marietta GA

Assignee:

The Trustees of the University of Pennsylvania - Philadelphia PA

International Classification:

A61N 118

US Classification:

607 45, 600544

Abstract:

A method and apparatus for forecasting and controlling neurological abnormalities in humans such as seizures or other brain disturbances. The system is based on a multi-level control strategy. Using as inputs one or more types of physiological measures such as brain electrical, chemical or magnetic activity, heart rate, pupil dilation, eye movement, temperature, chemical concentration of certain substances, a feature set is selected off-line from a pre-programmed feature library contained in a high level controller within a supervisory control architecture. This high level controller stores the feature library within a notebook or external PC. The supervisory control also contains a knowledge base that is continuously updated at discrete steps with the feedback information coming from an implantable device where the selected feature set (feature vector) is implemented. This high level controller also establishes the initial system settings (off-line) and subsequent settings (on-line) or tunings through an outer control loop by an intelligent procedure that incorporates knowledge as it arises. The subsequent adaptive settings for the system are determined in conjunction with a low-level controller that resides within the implantable device.

US Patent:

6658287, Dec 2, 2003

Filed:

May 18, 2001

Appl. No.:

09/762455

Inventors:

Brian Litt - Merion Station PA
George Vachtsevanos - Marietta GA
Javier Echauz - Dunwoody GA
Rosana Esteller - Marietta GA

Assignee:

Georgia Tech Research Corporation - Atlanta GA

International Classification:

A61B 504

US Classification:

600544

Abstract:

This invention is a method, and system for predicting the onset of a seizure prior to electrograph onset in an individual. During an âoff-lineâ mode, signals representing brain activity of an individual (either stored or real time) are collected, and features are extracted from those signals. A subset of features, which comprise a feature vector, are selected by a predetermined process to most efficiently predict (and detect) a seizure in that individual. An intelligent prediction subsystem is also trained âoff-lineâ based on the feature vector derived from those signals. During âon-lineâ operation, features are continuously extracted from real time brain activity signals to form a feacture vector, and the feature vector is continuously analyzed with the intelligent prediction subsystem to predict seizure onset in a patient. The system, and method are preferably implemented in an implanted device ( ) that is capable of warning externally an individual of the probability of a seizure, and/or automatically taking preventative actions to abort the seizure. In addition, methods are provided for applying intervention measures to an animal to abort or modulat a seizure by adjusting the modality of an intervention measure; and/or parameters of an intervention measure based upon a probability measure indicative of a likelihood of seizure occurrence; and/or a predicted time to seizure onset.

US Patent:

6678548, Jan 13, 2004

Filed:

Oct 20, 2000

Appl. No.:

09/693423

Inventors:

Javier Ramón Echauz - Atlanta GA
Rosana Esteller - Marietta GA
Brian Litt - Merion Station PA
George John Vachtsevanos - Marietta GA

Assignee:

The Trustees of the University of Pennsylvania - Philadelphia PA

International Classification:

A61B 504

US Classification:

600544

Abstract:

A method and an apparatus for predicting and detecting epileptic seizure onsets within a unified multiresolution probabilistic framework, enabling a portion of the device to automatically deliver a progression of multiple therapies, ranging from benign to aggressive as the probabilities of seizure warrant. Based on novel computational intelligence algorithms, a realistic posterior probability function P(S |x) representing the probability of one or more seizures starting within the next T minutes, given observations x derived from IEEG or other signals, is periodically synthesized for a plurality of prediction time horizons. When coupled with optimally determined thresholds for alarm or therapy activation, probabilities defined in this manner provide anticipatory time-localization of events in a synergistic logarithmic-like array of time resolutions, thus effectively circumventing the performance vs. prediction-horizon tradeoff of single-resolution systems. The longer and shorter prediction time scales are made to correspond to benign and aggressive therapies respectively.

US Patent:

7146218, Dec 5, 2006

Filed:

Feb 4, 2003

Appl. No.:

10/358039

Inventors:

Rosana Esteller - Marietta GA, US
Javier Ramón Echauz - Atlanta GA, US
Brian Litt - Merion Station PA, US
George John Vachtsevanos - Marietta GA, US

Assignee:

The Trustees of the University of Pennsylvania - Philadelphia PA

International Classification:

A61N 1/18
A61B 5/02

US Classification:

607 45, 600544

Abstract:

A method and apparatus for forecasting and controlling neurological abnormalities in humans such as seizures or other brain disturbances. The system is based on a multi-level control strategy. Using as inputs one or more types of physiological measures such as brain electrical, chemical or magnetic activity, heart rate, pupil dilation, eye movement, temperature, chemical concentration of certain substances, a feature set is selected off-line from a pre-programmed feature library contained in a high level controller within a supervisory control architecture. This high level controller stores the feature library within a notebook or external PC. The supervisory control also contains a knowledge base that is continuously updated at discrete steps with the feedback information coming from an implantable device where the selected feature set (feature vector) is implemented. This high level controller also establishes the initial system settings (off-line) and subsequent settings (on-line) or tunings through an outer control loop by an intelligent procedure that incorporates knowledge as it arises. The subsequent adaptive settings for the system are determined in conjunction with a low-level controller that resides within the implantable device.

US Patent:

7333851, Feb 19, 2008

Filed:

Sep 12, 2003

Appl. No.:

10/662072

Inventors:

Javier Ramón Echauz - Atlanta GA, US
Rosana Esteller - Marietta GA, US
Brian Litt - Merion Station PA, US
George John Vachtsevanos - Marietta GA, US

Assignee:

The Trustees of the University of Pennsylvania - Philadelphia PA

International Classification:

A61B 5/04

US Classification:

600544, 608378

Abstract:

A method and an apparatus for predicting and detecting epileptic seizure onsets within a unified multiresolution probabilistic framework, enabling a portion of the device to automatically deliver a progression of multiple therapies, ranging from benign to aggressive as the probabilities of seizure warrant. Based on novel computational intelligence algorithms, a realistic posterior probability function P(S|x) representing the probability of one or more seizures starting within the next T minutes, given observations x derived from IEEG or other signals, is periodically synthesized for a plurality of prediction time horizons. When coupled with optimally determined thresholds for alarm or therapy activation, probabilities defined in this manner provide anticipatory time-localization of events in a synergistic logarithmic-like array of time resolutions, thus effectively circumventing the performance vs. prediction-horizon tradeoff of single-resolution systems. The longer and shorter prediction time scales are made to correspond to benign and aggressive therapies respectively.

US Patent:

8065011, Nov 22, 2011

Filed:

Aug 31, 2006

Appl. No.:

11/469029

Inventors:

Javier Ramón Echauz - Atlanta GA, US
Brian Litt - Merion Station PA, US
Rosana Esteller - Marietta GA, US
George John Vachtsevanos - Marietta GA, US

Assignee:

The Trustees of the University of Pennsylvania - Philadelphia PA

International Classification:

A61N 1/00

US Classification:

607 45, 607 6, 607139

Abstract:

An adaptive method and apparatus for forecasting and controlling neurological abnormalities in humans such as seizures or other brain disturbances. The system is based on a multi-level control strategy. Using as inputs one or more types of physiological measures such as brain electrical, chemical or magnetic activity, heart rate, pupil dilation, eye movement, temperature, chemical concentration of certain substances, a feature set is selected off-line from a pre-programed feature library contained in a high level controller within a supervisory control architecture. This high level controller stores the feature library within a notebook or external PC. The supervisory control also contains a knowledge base that is continuously updated at discrete steps with the feedback information coming from an implantable device where the selected feature set (feature vector) is implemented. This high level controller also establishes the initial system settings (off-line) and subsequent settings (on-line) or tunings through an outer control loop by an intelligent procedure that incorporates knowledge as it arises. The subsequent adaptive settings for the system are determined in conjunction with a low-level controller that resides within the implantable device.

US Patent:

8086294, Dec 27, 2011

Filed:

Aug 13, 2007

Appl. No.:

11/837814

Inventors:

Javier Ramón Echauz - Atlanta GA, US
Brian Litt - Merion Station PA, US
Rosana Esteller - Marietta GA, US
George John Vachtsevanos - Marietta GA, US

Assignee:

The Trustees of the University of Pennsylvania - Philadelphia PA

International Classification:

A61B 5/04
G01N 33/48
G01N 31/00

US Classification:

600378, 600372, 702 19, 702 22

Abstract:

A method and system for assessing a quality of life index to adjust an implanted device to optimize patient-specific feature signals and treatment therapies. Accumulated energy of intracranial electroencephalogram (IEEG) signals is calculated over multiple data channels during seizures over a fixed time period. Accumulated energy of a treatment control is calculated over the multiple data channels over all times of activation of the implanted device over the fixed time period. The accumulated energy of both the IEEG signals and treatment control are weighted by seizure and treatment factors to determine a quality value for the fixed time period. A quality of life index is determined as a weighted average of current and previous quality values for a plurality of fixed time periods.

US Patent:

20020054694, May 9, 2002

Filed:

Mar 26, 1999

Appl. No.:

09/280145

Inventors:

GEORGE J. VACHTSEVANOS - MARIETTA GA, US
JAVIER ECHAUZ - MAYAGUEZ PR, US
MUID MUFTI - ATLANTA GA, US
J. LEWIS DORRITY - MARIETTA GA, US
PENG WANG - ATLANTA GA, US

International Classification:

G06K009/00
G06K009/62

US Classification:

382/111000, 382/156000

Abstract:

A method and apparatus is provided which analyzes an image of an object to detect and identify defects in the object utilizing multi-dimensional wavelet neural networks.