University of California

Mars Lan

Majid Sarrafzadeh

Myung-Kyung Suh

Hassan Ghasemzadeh

William J Kaiser

Hyduke Noshadi

Wenyao Xu

Jonathan S Woodbridge

Shaun S Ahmadian

Hagop Hagopian

Nabil Alshurafa

Navid Amini

Systems and methods for automatically analyzing and selecting prominent channels from multi-dimensional biomedical signals in order to detect particular diseases or ailments are provided. Such systems and methods may be applied in different ways to obtain numerous benefits, such as lowering of power and processing requirements, reducing an amount of data acquired, simplifying hardware deployment, detecting non-trivial patterns, obtaining, clinical episode prognosis, improving patient care, and/or the like.

Systems and methods for automatic segment selection for multi-dimensional biomedical signals

Systems, methods, and devices are disclosed that monitor the health status of patients. Dynamic task management functions apply data analytics to discretize continuous data values from monitored patients, and apply association rule mining techniques to prioritized required user tasks, to minimize the number of daily action items required by patients. The remaining action items maximize information gain, thereby improving the overall level of patient adherence and satisfaction without losing health monitoring effectiveness.

Task optimization in remote health monitoring systems

Systems and methods for generalized precursor pattern discovery that work with a wide range of biomedical signals and applications to detect a wide range of medical events are disclosed. In some embodiments, the methods and systems do not require domain-specific knowledge or significant reconfiguration based on the medical event being analyzed, hence it is also possible to discover patterns previously unknown to experts. In some embodiments, to build precursor pattern detection models, the system obtains annotated monitoring data. Positive and negative segments are extracted from the annotated monitoring data, and are preprocessed. Features are extracted from the preprocessed segments, and selected features are chosen from the extracted features. The selected features are classified to create the precursor pattern detection model The precursor pattern detection model may then be used in real time to detect occurrences of the medical event of interest.

Methods and systems for calculating and using statistical models to predict medical events

A method includes receiving contextual data related to at least one of environmental, physiological, behavioral, and historical context, and receiving outcome data related to at least one outcome. The method further includes creating a feature set from the contextual data, selecting a subset of features from the feature set, assigning a score to each feature in the subset of features according to the probability that the feature is a predictor of the at least one outcome, and generating a characteristic curve for the at least one outcome from the subset of features, the characteristic curve being based on the scoring. The method further includes calculating the area under the characteristic curve, and using, the area under the characteristic curve, identifying whether the subset of features is a suitable predictor for the at least one outcome.

Context-aware prediction in medical systems

A method for determining the motive instability of an individual using foot pressure, foot speed and foot direction data collected from sensors on shoes. The sensed data is used to determine the minimum number and the placement of pressure sensors in the shoe. The data from the sensors is processed to extract spatial and temporal parameters as desired. The data is grouped into segments based on a segmentation rule. The trend in each segment is determined. The variability of the trend in each segment is determined. The risk of fall is computed on the basis of the trend and variance. The computation is adjustable by emphasizing certain parameters in order to tailor the instability assessment to a specific individual.

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Los Angeles, CA, United States of America

Mars Lan