Abstract: 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.

Abstract: 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 {y1, . . . , yN}, where yj denotes the jth observation at time tj, 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 ?=(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 zt which, in turn, is used to forecast an unknown observation yN+1 or to impute an unknown observation yt, where 1<t<N. The historical data is then updated with the forecasted observation yN+1 or the imputed unknown observation yt.

Abstract: 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.

Abstract: 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.

Abstract: 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?10 seconds of video. For each image frame of the first portion, flow vectors Ft are 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 Ft(x,y) are determined for each (x,y) pixel location and a projection of Ft along 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.

Abstract: 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?10 seconds of video. For each image frame of the first portion, flow vectors Ft are 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 Ft(x,y) are determined for each (x,y) pixel location and a projection of Ft along 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 {y1. . . , yN where yN denotes the jth observation at time tj 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 ?=(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 zt which, in turn, is used to forecast an unknown observation yN+1 or to impute an unknown observation yt, where 1<t<N. The historical data is then updated with the forecasted observation yN+1 or the imputed unknown observation yt.

Abstract: What is disclosed is a system and method for determining when a subject is speaking from a respiratory signal obtained from a video of that subject. A video of a subject is received and a respiratory signal is extracted from a time-series signal is obtained from processing pixels in image frames of the video. The respiratory signal comprises an inspiratory signal and an expiratory signal. Cycle-level feature are extracted from the respiratory signal and used to identify expiratory signals during which speech is likely to have occurred. The identified expiratory signal are divided into time intervals. Frame-level features are determined for each time interval and an amount of distortion in the expiratory signal for this time interval is quantified. The amount of distortion is compared to a threshold. In response to the comparison, a determination is made that speech occurred during this interval. The process repeats for all time intervals.

Abstract: 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.

Abstract: 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.

Abstract: 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.