Xerox Corporation

Conduent Business Services, LLC

Prathosh Aragulla Prasad

Vaibhav Rajan

Himanshu Jayantkumar Madhu

Stephen Craig Fullerton

Abhishek Sengupta

Katerina Sinclair

Lalit Keshav Mestha

Vivek Tyagi

Bhupendra Singh Solanki

Satya Narayan Shukla

Pragathi Praveena

Sakyajit Bhattacharya

Vijay Huddar

Harish Arsikere

Arunasish Sen

James D Kirkendall

Sanjay Bharadwaj

Tejaskumar Dipaklal Bengali

Satish Prasad Rath

Avishek Chatterjee

A method and a system for predicting admission of a human subject to a first ward in a medical center are disclosed. A patient dataset is generated based on at least a measure of one or more physiological parameters associated with one or more first human subjects and a first information pertaining to the admission of each of the one or more first human subjects to the first ward. For a first human subject of the one or more first human subjects, a first score at each of the one or more first time instants is determined. Further, one or more second time instants from the one or more first time instants are identified. Further, a second score at each of the one or more second time instants is determined. In an embodiment, the first classifier is trained based on at least the second score, and the first information.

Method and system for predicting admission of a human subject to a ward in a medical center

What is disclosed is a system and method for forecasting and imputing an unknown vital measurement of a patient. Temporally successive patient vital measurements are received which comprise irregularly sampled observations {y, . . . , y}, where ydenotes the jobservation at time t, and N is the number of samples. The vital measurements are then provided to a model trained using historical data of patient vital measurements. The model generates a parameter set &#x3b8;=(A,B,C), where A is a state transition matrix, B is a control matrix, and C is a matrix which maps state-space variables to observation variables. The parameters are used to obtain state-space variable zwhich, in turn, is used to forecast an unknown observation yor to impute an unknown observation y, where 1&#x3c;t&#x3c;N. The historical data is then updated with the forecasted observation yor the imputed unknown observation y.

Forecasting a patient vital measurement for healthcare analytics

Systems and methods of modeling irregularly sampled time series signals with unknown temporal dynamics are disclosed wherein a temporal difference variable (TDV) is introduced to model irregular time differences between subsequent measurements. A hierarchical model is designed comprising two linear dynamical systems that model the effects of evolving TDV on temporal observations. All the parameters of the model, including the temporal dynamics, are statistically estimated using historical data.

System and method of modeling irregularly sampled temporal data using Kalman filters

The disclosed embodiments illustrate a method for classifying one or more audio segments of an audio signal. The method includes determining one or more first features of a first audio segment of the one or more audio segments. The method further includes determining one or more second features based on the one or more first features. The method includes determining one or more third features of the first audio segment, wherein each of the one or more third features is determined based on a second feature of the one or more second features of the first audio segment and at least one second feature associated with a second audio segment. Additionally, the method includes classifying the first audio segment either in an interrogative category or a non-interrogative category based on one or more of the one or more second features and the one or more third features.

Methods and systems for classifying audio segments of an audio signal

What is disclosed is a system and method for processing a video to extract a periodic signal which was captured by the video imaging device. One embodiment involves the following. First, a video of a subject in a scene is received. The video is acquired by a video imaging device. There is an underlying motion signal in the scene corresponding to cardiac or respiratory function. A time-series signal is generated for each pixel or for each group of pixels in a temporal direction across a plurality of image frames. Time-series signals of interest are selected. The selected time-series signals of interest are then processed to obtain a periodic signal corresponding to cardiac or respiratory function. The present method has a low computational complexity, is robust in the presence of noise, and finds its uses in applications requiring real-time motion quantification of a signal in a video.

System and method for extracting a periodic signal from video

What is disclosed is a system and method for determining a respiration rate from a video of a subject breathing. One embodiment of the present method involves the following. A video is received of a subject breathing which comprises a first portion of N image frames, and a second portion of M image frames, N+M=T and N&#x2267;10 seconds of video. For each image frame of the first portion, flow vectors Fare determined for each (x,y) pixel location. A correlated flow field V is then calculated for the first portion of video. For each image frame of the second portion, flow vectors F(x,y) are determined for each (x,y) pixel location and a projection of Falong V is calculated to obtain a velocity of thoracoabdominal motion in the direction of V. The velocity is integrated to obtain an integrated signal. Respiration rate is determined from the integrated signal.

Determining respiration rate from a video of a subject breathing

A method and a system for detecting sentiment of a human based on an analysis of human speech are disclosed. In an embodiment, one or more time instances of glottal closure are determined from a speech signal of the human. A voice source signal based on the determined one or more time instances of glottal closure is generated. A set of relative harmonic strengths is determined based on one or more harmonic contours of the voice source signal. The RHS is indicative of a deviation of the one or more harmonics of the voice source signal from a fundamental frequency of the voice source signal. A set of feature vectors is determined based on the RHS. The set of feature vectors are utilizable to detect the sentiment of the human.

Method and system for detecting sentiment by analyzing human speech

The disclosed embodiments relate to a method of keyword recognition in a speech signal. The method includes determining a first likelihood score and a second likelihood score of one or more features of a frame of said speech signal being associated with one or more states in a first model and one or more states in a second model, respectively. The one or more states in the first model corresponds to one or more tied triphone states and the one or more states in the second model corresponds to one or more monophone states of a keyword to be recognized in the speech signal. The method further includes determining a third likelihood score based on the first likelihood score and the second likelihood score. The first likelihood score and the third likelihood score are utilizable to determine presence of the keyword in the speech signal.

Methods and systems for identifying keywords in speech signal

A video is received of a region of a subject where a signal corresponding to respiratory function can be registered by a video device. Pixels in the region in each of the image frames are processed to identify a respiratory pattern with peak/valley pairs. A peak/valley pair of interest is selected. An array of optical flow vectors is determined between a window of groups of pixel locations in a reference image frame corresponding to a peak of the pair/valley pair and a window in each of a number of image frames corresponding to the respiratory signal between the peak and ending at a valley point. Optical flow vectors have a direction and a magnitude. A ratio is determined between upwardly pointing optical flow vectors and downwardly pointing optical flow vectors. Based on the ratio, a determination is made whether the respiration phase for that peak/valley pair is inspiration or expiration.

Estimating respiratory phase from a video of a subject

What is disclosed is a system and method for determining a subject's respiratory pattern from a video of that subject. One embodiment involves receiving a video comprising N&#x2267;2 time-sequential image frames of a region of interest (ROI) of a subject where a signal corresponding to the subject's respiratory function can be registered by at least one imaging channel of a video imaging device. The ROI comprises P pixels. Time-series signals of duration N are generated from pixels isolated in the ROI. Features are extracted from the time-series signals and formed into P-number of M-dimensional vectors. The feature vectors are clustered into K clusters. The time-series signals corresponding to pixels represented by the feature vectors in each cluster are averaged along a temporal direction to obtain a representative signal for each cluster. One of the clusters is selected. A respiratory pattern is determined for the subject based on the representative signal.

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Prathosh Aragulla Prasad