John V Guttag

8346349, Jan 1, 2013

Jan 15, 2009

12/321239

John V. Guttag - Lexington MA, US
Zeeshan H. Syed - Wayzata MN, US
Philip Pohong Sung - Saratoga CA, US
Collin M. Stultz - Newton MA, US

Massachusetts Institute of Technology - Cambridge MA

A61B 5/04

600509

A method and apparatus for predicting patient outcome from a physiological segmentable signal of a patient. In one embodiment, the method comprises the steps of obtaining the physiological segmentable signal of the patient; segmenting the physiological segmentable signal into a plurality of separate segmentable components; calculating a time series of the morphological distance between adjacent separate segmentable components of the plurality of separate segmentable components; and predicting patient outcome in response to the time series of the morphological distance. In another aspect, the invention relates to a method for extracting information from physiological signals for one or more subjects including the steps of partitioning the physiological signal into a plurality of components, grouping the components into a plurality of information classes, assigning a unique symbol to each information class, mapping each component to the assigned symbol, and examining one or more such sequences for clinical significance.

20040260188, Dec 23, 2004

Jun 17, 2003

10/464267

Zeeshan Syed - Wayzata MN, US
John Guttag - Lexington MA, US
Robert Levine - Brookline MA, US
Francesca Nesta - Boston MA, US
Dorothy Curtis - Framingham MA, US

A61B005/04

600/509000, 600/513000, 600/528000

The present invention provides systems and methods for performing automated auscultation and diagnosis of conditions of the cardiovascular system. The invention acquires an acoustic signal emanating from the cardiovascular system via an sensor. In addition, in certain embodiments of the invention an electrical signal, e.g., an electrocardiogram (EKG) is simultaneously acquired. The signals are digitized, and, optionally, filtered to remove noise. The invention then processes and analyses the signal(s) so as to provide a clinically relevant conclusion or recommendation such as a diagnosis or suggested additional tests or therapy. In preferred embodiments the system comprises: (1) a beat selection component that selects a plurality of beats for analysis, wherein each beat comprises an acoustic signal emanating from the cardiovascular system; (2) a time-frequency analysis component that performs a time-frequency decomposition of beats selected for analysis so as to identify or extract physiologically relevant features; and (3) a processing component that processes the information so at to provide a clinically relevant conclusion or recommendation. In preferred embodiments of the invention the system further includes an aggregation component that combines information obtained from a plurality of the beats selected for analysis. In one embodiment, the system diagnoses mitral valve prolapse.

20060111644, May 25, 2006

May 27, 2005

11/140551

John Guttag - Lexington MA, US
Ali Shoeb - Winchester MA, US
Blaise Bourgeois - Newton MA, US
S. Treves - Wellesley MA, US
Steven Schachter - Sharon MA, US
Herman Edwards - Watertown MA, US
John Connolly - Norton MA, US

CHILDREN'S MEDICAL CENTER CORPORATION - Boston MA
MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT) - Cambridge MA

A61B 5/04

600544000

The present invention provides methods and systems for patient-specific seizure onset detection. In one embodiment, at least one EEG waveform of the patient is recorded, and at least one epoch (sample) of the waveform is extracted. The waveform sample is decomposed into one or more subband signals via a wavelet decomposition of the waveform sample, and one or more feature vectors are computed based on the subband signals. A seizure onset can then be identified based on classification of the feature vectors to a seizure or a non-seizure class by comparing the feature vectors with a decision measure previously computed for that patient. The decision measure can be derived based on reference seizure and non-seizure EEG waveforms of the patient. In another aspect, similar methodology is employed for automatic detection of alpha waves. In other aspects, the invention provides diagnostic and imaging systems that incorporate the above seizure-onset and alpha-wave detection methodology.

20090082689, Mar 26, 2009

Aug 22, 2008

12/196690

John V. Guttag - Lexington MA, US
Ali Shoeb - Winchester MA, US
Elena L. Glassman - Pipersville PA, US
Eugene I. Shih - Cambridge MA, US

A61B 5/0478

600544

An ambulatory patient-specific epileptic seizure detector based on scalp EEG signals is presented. A method for selecting a patient-specific subset of electrodes from a plurality of m EEG channels needed to detect an epileptic seizure in the patient is also presented. Seizure EEG data is collected from the plurality of m EEG channels. An effective subset n of the channels of the plurality of m EEG channels is selected using recursive feature processing and a detector is constructed in response to the subset n of channels. The performance of the detector in detecting seizures is then estimated.

20100016743, Jan 21, 2010

Jul 16, 2009

12/504543

Zeeshan H. Syed - Wayzata MN, US
John V. Guttag - Lexington MA, US
Collin M. Stultz - Newton MA, US

A61B 5/0402

600509

The invention relates in part to methods for partitioning a plurality of patients into risk profile groups comprising the steps of: recording a physiological signal from each patient of a plurality of patients; segmenting the physiological signal into a plurality of components for each patient of a plurality of patients; grouping the components into a plurality of information classes for each patient of a plurality of patients; assigning a representation to each information class for each patient of a plurality of patients; and grouping the patients in response to the representations of their respective information classes.

20100016748, Jan 21, 2010

Jul 16, 2009

12/504529

Zeeshan H. Syed - Wayzata MN, US
John V. Guttag - Lexington MA, US
Collin M. Stultz - Newton MA, US

A61B 5/0432
G06N 5/02

600523, 706 52, 706 54

The application relates a methodology and apparatus for identifying predictive patterns for acute clinical events in the absence of prior knowledge. Principles of conservation are used to identify activity that consistently precedes an outcome in patients, and describe a two-stage process that allows us to more efficiently search for such patterns in large datasets. This is achieved by first transforming continuous physiological signals from multiple patients into symbolic sequences, and by then searching for patterns in these reduced representations that are strongly associated with an outcome.

20110257517, Oct 20, 2011

Jun 6, 2011

13/153819

John V. Guttag - Lexington MA, US
Ali Hossam Shoeb - Winchester MA, US
Blaise Bourgeois - Newton MA, US
S. Ted Treves - Wellesley MA, US
Steven C. Schachter - Sharon MA, US
Herman A. Edwards - Watertown MA, US
John Connolly - Norton MA, US

A61B 5/0476
A61B 6/03
A61B 6/00

600425, 600544, 600431

The present invention provides methods and systems for patient-specific seizure onset detection. In one embodiment, at least one EEG waveform of the patient is recorded, and at least one epoch (sample) of the waveform is extracted. The waveform sample is decomposed into one or more subband signals via a wavelet decomposition of the waveform sample, and one or more feature vectors are computed based on the subband signals. A seizure onset can then be identified based on classification of the feature vectors to a seizure or a non-seizure class by comparing the feature vectors with a decision measure previously computed for that patient. The decision measure can be derived based on reference seizure and non-seizure EEG waveforms of the patient. In another aspect, similar methodology is employed for automatic detection of alpha waves. In other aspects, the invention provides diagnostic and imaging systems that incorporate the above seizure-onset and alpha-wave detection methodology.

20140072190, Mar 13, 2014

Mar 26, 2013

13/850717

Michael Rubinstein - Somerville MA, US
Eugene Inghaw Shih - Brookline MA, US
John V. Guttag - Lexington MA, US
Frederic Durand - Somerville MA, US
William T. Freeman - Acton MA, US

Massachusetts Institute of Technology - Cambridge MA

G06T 7/00

382128

In one embodiment, a method of amplifying temporal variation in at least two images comprises examining pixel values of the at least two images. The temporal variation of the pixel values between the at least two images can be below a particular threshold. The method can further include applying signal processing to the pixel values.