Siemens Medical Solutions USA, Inc.

Shenzhen Mindray Bio-Medical Electronics Co., Ltd.

Spacelabs Healthcare

Mr Holdings (Hk) Limited

Diane S Paine

Dorin Comaniciu

Xiang Sean Zhou

Bhavani Duggirala

Visvanathan Ramesh

Arun Krishnan

Alok Gupta

Daniel J Wilson

Kyril Feldman

William Oren Wekell

Robert Boyer Koenig

Patricia Walters

Bob Steurer

Vicki Childress

Kim Apker

Al Roxin

Systems and methods are provided for quantifying and providing indicia of ST-segment resolution in an electrocardiogram (ECG) signal. A receiver acquires an electrocardiogram (ECG) signal that includes an ST-segment. A processor processes the ECG signal to determine values for the ST-segment deviation relative to an isoelectric baseline. A user is allowed to provide a baseline signal to the processor. The processor responds to the baseline signal by marking a baseline ST-segment value corresponding to a baseline time. A user interface displays a linear graphical trend of variations in the measured ST-segment values relative to the baseline ST-segment value. In certain embodiments, the processor detects user-selected trigger events such as post-intervention ST deviation relative to the baseline time and the baseline ST-segment value, and provides indicia of the trigger event. In addition, or in other embodiments, a verbal annunciation of a percent ST-segment resolution is provided.

Systems and methods for quantifying and providing indicia of ST-segment resolution in an ECG signal

The present invention is a novel method of generating and representing the status of various physiological parameters that are monitored for patients during hospitalization. The system of present invention allows healthcare providers to easily view, at a glance, the status or trend of a patient or a plurality of patients as well as any changes in the parameter values.

Trending display of patient wellness

A system and method for providing decision support to a physician during a medical examination is disclosed. Data is received from a sensor representing a particular medical measurement. The received data includes image data. The received data and context data is analyzed with respect to one or more sets of training models. Probability values for the particular medical measurement and other measurements to be taken are derived based on the analysis and based on identified classes. The received image data is compared with training images. Distance values are determined between the received image data and the training images, and the training images are associated with the identified classes. Absolute value feature sensitivity scores are derived for the particular medical measurement and other measurements to be taken based on the analysis. The probability values, distance values and absolute value feature sensitivity scores are outputted to the user.

System and method for performing probabilistic classification and decision support using multidimensional medical image databases

Various attributes for medical CAD analysis used alone or in combination are disclosed, including: (1) using medical sensor data and context information associated with the configuration of the medical system to provide recommendations for further acts to be performed for more accurate or different diagnosis, (2) recommending further acts to be performed and providing an indication of the importance of the further act for further diagnosis, (3) distributing the computer-assisted diagnosis software and/or database over multiple systems, and (4) transmitting patient data to a remote system for CAD diagnosis and using the results of the CAD diagnosis at the remote facility.

Computer aided diagnostic assistance for medical imaging

A system and method for assigning feature sensitivity values to a set of potential measurements to be taken during a medical procedure of a patient in order to provide a medical diagnosis is disclosed. Data is received from a sensor that represents a particular medical measurement. The received data and context data are analyzed with respect to one or more sets of training models. Feature sensitivity values are derived for the particular medical measurement and other potential measurements to be taken based the analysis, and the feature sensitivity values are outputted.

System and method for real-time feature sensitivity analysis based on contextual information

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Redmond, WA, United States of America

Diane S Paine