Visualizing Multivariate Time Series Data to Detect Specific Medical Conditions

Links to Files

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2656052/

Permanent Link

http://hdl.handle.net/11603/38687

Collections

UMBC Faculty Collection
UMBC Computer Science and Electrical Engineering Department
UMBC Information Systems Department

Author/Creator

Author/Creator ORCID

Date

2008

Type of Work

conference papers and proceedings
Text

Department

Program

Citation of Original Publication

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

detection of patterns in time series data
Symbolic Aggregate Approximation (SAX)
detecting anomalies in ECG readings
Multivariate Time Series Amalgam (MTSA)
Efficient unsupervised algorithms

Abstract

Efficient unsupervised algorithms for the detection of patterns in time series data, often called motifs, have been used in many applications, such as identifying words in different languages, detecting anomalies in ECG readings, and finding similarities between images. We present a process that creates a personalized multivariate time series representation—a Multivariate Time Series Amalgam (MTSA) — of physiological data and laboratory results that physicians can visually interpret. We then apply a technique that has demonstrated success with the interpretation of univariate data, named Symbolic Aggregate Approximation (SAX), to visualize patterns in the MTSAs that may differentiate between medical conditions such as renal and respiratory failure.