Koninklijke Philips Corporation N.V.

Walid S I Ali

Larry James Eshelman

Yasser Alsafadi

Larry Nielsen

Cornelis Van Zon

Mohammed Saeed

Joseph James Frassica

Wei Zong

Omar Abdala

In a patient monitoring system (), shorter interval physiological parameters and longer interval clinical data are collected from a monitored patient (). A composite acuity score generator () generates or updates a composite acuity score indicative of wellbeing of the patient () based at least on the sensed physiological parameters and the longer interval data. A monitor () displays current values of at least one of selected sensed physiological parameters, longer interval data, and the composite acuity score.

Apparatus to measure the instantaneous patients' acuity value

Described herein is a patient monitoring system that includes a body network () with at least one sensor () that senses physiological information about a patient and a cognitive device () for communicating the physiological information to a remote location. The cognitive device includes a cognitive radio (), a cognitive monitor (), and a transmitter (). The cognitive radio () checks detected frequency spectra () for unused bandwidth and recommends one or more bands on which to transmit clinically relevant information received from the body network () to the remote location; the cognitive monitor () receives the information from the body network (), prioritizes the information based at least in part on a set of rules (), and selects which information to transmit based on the prioritization and the recommended transmission bands; and the transmitter () transmits the selected information as a junction of priority over at least one or the recommended transmission bands.

Wireless medical monitoring device

A patient physiological information monitoring system includes a plurality of patient monitoring devices () and a physiological information analyzer (). The plurality of patient monitoring devices () monitor physiological information from a patient and generate corresponding physiological signals. The physiological information analyzer () processes the monitored physiological information and determines whether a physiological change is a clinically significant event or an artifact. The physiological information analyzer includes at least one receiver () that receives the physiological signals from the patient monitoring devices; a signal correlator () that generates morphograms from pairs of the received physiological signals; a signature generator () that applies a wavelet decomposition to each morphogram to compute a signature for each morphogram; and a decision component () that compares the morphogram signatures within and across sampling intervals and determines if a physiological change is a clinically significant change or an artifact.

Method and apparatus for distinguishing between clinically significant changes and artifacts in patient physiological information

A method for combining a random set of video features non-linearly to evaluate perceptual quality of video sequences includes (a) receiving a video sequence for image quality evaluation; (b) providing an objective metric image quality controller comprising a random set of metrics ranging from Mto Mwithout dependency information for each one metric; (c) applying each one metric individually to the video sequence to provide an individual objective scoring value of the video sequence ranging from xto x; (d) determining a plurality of sets of weights (wto w) which correlate to predetermined subjective evaluations of image quality for a predetermined plurality of video sequences (n), each one set of weights being assigned a range having an incremental value equal to the range divided by a number of combinations for each one set of weights; (e) weighting each individual objective scoring value xto xprovided by each one metric of the random set of metrics in step (c); (f) combining metrics of the weighted individual objective scoring value of the random set of metrics into a single objective evaluation F, wherein each weighted individual scoring value from step (e) is multiplied by each individual objective scoring value xto xfrom step (c); (g) calculating a correlation factor R to provide a correlation value for the objective evaluation F and the plurality of video sequences (n). Steps (e), (f) and (g) are repeated to provide a plurality of correlation factors which are ranked. A heuristic search uses a genetic algorithm to find the best set of weights to provide objective scores closest to predetermined subjective evaluations. A system provides the hardware and modules that perform the non-linear combination of metrics to provide enhanced perceptual image information.

Apparatus and method for combining random set of video features in a non-linear scheme to best describe perceptual quality of video sequences using heuristic search methodology

An optimizing video processing method and system selects algorithms for best obtainable video quality for the available computation resources. A video processing module, which processes an input of a video stream, architectural parameters for identifying an order of cascaded video functions and determining a bit precision between data of any consecutive cascaded functions according to an associated complexity level which correlates with a value of available computational resources. An optimizer module optimizes processing of the video stream and includes a plurality of optimization engines each having an associated complexity level. The optimizer module selects an optimization engine according a complexity level which correlates with the value of available computational resources. An Object Image Quality (OIQ) evaluator module evaluates an image quality of an output of the video stream from the video processing module. The OIQ evaluator module includes a plurality of objective image quality metrics having an associated complexity level. The OIQ evaluator module selects a metric according to a correlation factor and a complexity level for said value of available computation resources.

Scalable expandable system and method for optimizing a random system of algorithms for image quality

There is disclosed an improved system and method for providing a scalable dynamic objective metric for automatically evaluating the video quality of a video image. The system comprises an objective metric controller that is capable of receiving a plurality of objective metric figures of merit from a plurality of objective metric model units. The system determines a scalable dynamic objective metric from a weighted average of the plurality of objective metric figures of merit. The scalable dynamic objective metric represents the best correlation of objective metric measurements of the video image with subjective measurements of the video image. The weight value of individual objective metric figures of merit may be increased or decreased depending upon the type of video image being evaluated. Individual objective metric figures of merit may be added to the system or deleted from the system. The system is capable of continually determining a new value of the scalable dynamic objective metric as the plurality of objective metric model units receive new video images.

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Chandler, AZ, United States of America

Walid S I Ali