Browsing by Subject "Event detection"

Now showing 1 - 2 of 2
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    Approximate search on massive spatiotemporal datasets.
    (2012-08) Brugere, Ivan
    Efficient time series similarity search is a fundamental operation for data exploration and analysis. While previous work has focused on indexing progressively larger datasets and has proposed data structures with efficient exact search algorithms, we motivate the need for approximate query methods that can be used in interactive exploration and as fast data analysis subroutines on large spatiotemporal datasets. This thesis formulates a simple approximate range query problem for time series data, and proposes a method that aims to quickly access a small number of high-quality results of the exact search resultset. We formulate an anytime framework, giving the user flexibility to return query results in arbitrary cost, where larger runtime incrementally improves search results. We propose an evaluation strategy on each query framework when the false dismissal class is very large relative to the query resultset and investigate the performance of indexing novel classes of time series subsequences.
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    Event detection for post lung transplant based on home monitoring of spirometry and symptoms
    (2011-12) Wang, Xuewe
    The goal of this dissertation research was to develop, implement, and test an automated decision system to provide early detection of actual acute bronchopulmonary events in a population of lung transplant recipients following a home monitoring protocol. Decision rules were developed using wavelet analysis of spirometry and symptom signal data collected daily at home by the lung transplant recipients, and transmitted weekly to our study data center. Rules were developed based on a learning set of patient home data, and validated with an independent set of patients. Using either FEV1 or symptom-based home data monitoring, the detection algorithm can capture the majority of events (sensitivity > 80%) at an acceptable level of false alarms. Detection occurs 6.6 to 10.8 days earlier than the corresponding events recorded in the patient's clinical records. Combining rules using the Dempster-Shafer theory of evidence incrementally improves performance over a single variable. This framework can be readily implemented as an automatic event detection tool to aid medical discovery and diagnosis of acute pulmonary events.