Abstract: Systems and methods for monitoring uterus contraction activity and progress of labor. The system of the subject invention can comprises (1) a plurality of sensors; (2) an amplifying/filtering means; (3) a computing means; and (4) a graphical user interface. Accurate clinical data, which can be extracted and provided to the user in real-time using the system of the invention, include without limitation, progress of labor, prediction and monitoring of preterm labor, and intrauterine pressure prediction. In a preferred embodiment, the system of the invention includes an intelligence means, such as a neural network system, to analyze and interpret clinical data for use in clinical diagnosis as well as delivery strategy.

Abstract: A system and method for monitoring a patient's compliance with a medication regimen includes an electronic tag integral with or attached to a medicine delivery device such as a capsule, the tag having an antenna and a receiver/transmitter, the system also including a reader positioned externally for detecting the presence and location of the delivery device in the patient.

Abstract: A system and method for monitoring a patient's compliance with a medication regimen includes an electronic tag having an antenna and a receiver/transmitter positioned with a medication capsule, and a reader positioned externally for detecting the presence and location of the tag in the patient.

Abstract: Various examples are provided for automation of QRS detection. In one example, among others, a system includes an integrate and fire (IF) sampler that can generate an IF pulse train from an analog input signal, and decision logic circuitry that can determine whether a QRS complex waveform is present in a pulse segment of the IF pulse train. In another example, a method includes generating an integrate and fire (IF) pulse train from an analog input signal, identifying a pulse segment of the IF pulse train, and determining whether a QRS waveform is present in the pulse segment based at least in part upon attributes associated with the pulse segment.

Abstract: Various examples are provided for object recognition in video. In one example, among others, a system includes processing circuitry including a processor. The processing circuitry is configured to process a sequence of images to recognize an object in the images, the recognition of the object based upon a hierarchical model. In another example, a method includes determining input data from a plurality of overlapping pixel patches of a video image; determining a plurality of corresponding states based at least in part upon the input data and an over-complete dictionary of filters; and determining a cause based at least in part upon the plurality of corresponding states. The cause may be used to identify an object in the video image.

Abstract: Various examples are provided for brain state advisory systems using calibrated metrics and optimal time-series decompositions. In one embodiment, a method includes monitoring brainwave activity of a subject and classifying a brain state of the subject based upon the brainwave activity and a model of the brainwave activity. In another embodiment, brain states of the subject are modeled based upon the brainwave activity of the subject.

Abstract: An example method for anomaly detection in streaming data includes applying statistical analysis to streaming data in a sliding window. The method also includes extracting a feature. The method also includes determining class assignment for the feature using class conditional probability densities and a threshold.

Abstract: Various examples are provided for object recognition in video. In one example, among others, a system includes processing circuitry including a processor. The processing circuitry is configured to process a sequence of images to recognize an object in the images, the recognition of the object based upon a hierarchical model. In another example, a method includes determining input data from a plurality of overlapping pixel patches of a video image; determining a plurality of corresponding states based at least in part upon the input data and an over-complete dictionary of filters; and determining a cause based at least in part upon the plurality of corresponding states. The cause may be used to identify an object in the video image.

Abstract: Regime change in streaming data is detected. The streaming data is sent to a plurality of modules. Each module in the plurality of modules produces an association measure. The association measure is a measure of similarity between the streaming data and regime data associated with the module producing the association measure. A regime change in the streaming data is detected based on values of the association measures from the plurality of modules.

Abstract: A Brain Machine Interface (BMI) agent (110) is provided, that when operatively coupled to a subject during a mental task by the subject to control a prosthetic device (130), monitors one or more states (125) of neural activity of the subject, receives feedback associated with a behavior of the prosthetic device responsive to the control, learns a functional mapping between the mental task and the behavior in view of the feedback, and applies at least one control action (115) to the prosthetic device in accordance with the learning to control the prosthetic device for a targeted behavior.

Abstract: A system and method for monitoring a patient's compliance with a medication regimen includes an electronic tag integral with or attached to a medicine delivery device such as a capsule, the tag having an antenna and a receiver/transmitter, the system also including a reader positioned externally for detecting the presence and location of the delivery device in the patient.

Abstract: Various examples are provided for automation of QRS detection. In one example, among others, a system includes an integrate and fire (IF) sampler that can generate an IF pulse train from an analog input signal, and decision logic circuitry that can determine whether a QRS complex waveform is present in a pulse segment of the IF pulse train. In another example, a method includes generating an integrate and fire (IF) pulse train from an analog input signal, identifying a pulse segment of the IF pulse train, and determining whether a QRS waveform is present in the pulse segment based at least in part upon attributes associated with the pulse segment.

Abstract: A system and method for classifying unclassified samples. The method includes detecting a number of classes including training samples in training data sets. The method includes, for each class, determining a vector for each training sample based on a specified number of nearest neighbor distances between the training sample and neighbor training samples, and determining a class distribution based on the vectors. The method also includes detecting an unclassified sample in a data set and, for each class, determining a vector for the unclassified sample based on the specified number of nearest neighbor distances between the unclassified sample and nearest neighbor training samples within the class, and determining a probability that the unclassified sample is a member of the class based on the vector and the class distribution. The method further includes classifying the unclassified sample based on the probabilities.

Abstract: A system and method for classifying unclassified samples. The method includes detecting a number of classes including training samples in training data sets. The method includes, for each class, determining a vector for each training sample based on a specified number of nearest neighbor distances between the training sample and neighbor training samples, and determining a class distribution based on the vectors. The method also includes detecting an unclassified sample in a data set and, for each class, determining a vector for the unclassified sample based on the specified number of nearest neighbor distances between the unclassified sample and nearest neighbor training samples within the class, and determining a probability that the unclassified sample is a member of the class based on the vector and the class distribution. The method further includes classifying the unclassified sample based on the probabilities.

Abstract: A method (200) for detecting a periodic signal (141) in a noisy signal (101) is provided. The method can include applying (210) correntropy to the noisy signal to generate a non-linear mapping, and applying (220) a sub-space projection to the non-linear mapping to produce principal components. A correntropy kernel can be applied to the noisy signal to generate a Gram matrix that is used in a Temporal Principal Component Analysis (TPCA). The correntropy kernel projects nonlinearly the input data to a reproducing kernel Hilbert Space (RKHS) preserving the input time structure and attenuating impulsive noise. The correntropy kernel is data dependent, and the RKHS correlation matrix has the same dimension as the input data correlation matrix. A principal component having a majority of signal energy can be chosen (230) to detect the periodic signal.

Abstract: Systems and methods for anomaly detection in streaming data are disclosed. An example method includes applying statistical analysis to streaming data in a sliding window. The method also includes extracting a feature. The method also includes determining class assignment for the feature using class conditional probability densities and a threshold.

Abstract: Regime change in streaming data is detected. The streaming data is sent to a plurality of modules. Each module in the plurality of modules produces an association measure. The association measure is a measure of similarity between the streaming data and regime data associated with the module producing the association measure. A regime change in the streaming data is detected based on values of the association measures from the plurality of modules.

Abstract: Systems and methods of time encoding using an integrate and fire (IF) sampler are disclosed. In an example, a method includes receiving input signals for separate classes. The method also includes generating a pulse train based on the input signals. The method also includes binning the pulse train to generate a feature vector.

Abstract: A maternal-fetal monitoring system for use during all stages of pregnancy, including antepartum and intrapartum stages. The maternal-fetal monitoring system of the subject invention comprises (1) a set of sensors; (2) an amplifying/filtering means; (3) a computing means; and (4) a graphical user interface. Accurate clinical data, which can be extracted and provided to the user in real-time using the system of the invention, include without limitation, maternal electrocardiogram (ECG) signals, maternal uterine activity signals (EHG), maternal heart rate, fetal ECG signals, and fetal heart rate. In a preferred embodiment, the maternal-fetal monitoring system of the invention includes an intelligence means, such as a neural network system, to analyze and interpret clinical data for use in clinical diagnosis antepartum, intrapartum and postpartum, as well as delivery strategy.

Abstract: Systems and methods for monitoring uterus contraction activity and progress of labor. The system of the subject invention can comprises (1) a plurality of sensors; (2) an amplifying/filtering means; (3) a computing means; and (4) a graphical user interface. Accurate clinical data, which can be extracted and provided to the user in real-time using the system of the invention, include without limitation, progress of labor, prediction and monitoring of preterm labor, and intrauterine pressure prediction. In a preferred embodiment, the system of the invention includes an intelligence means, such as a neural network system, to analyze and interpret clinical data for use in clinical diagnosis as well as delivery strategy.