Computational methods for physiological data

Large volumes of continuous waveform data are now collected in hospitals. These datasets provide an opportunity to advance medical care, by capturing rare or subtle phenomena associated with specific medical conditions, and by providing fresh insights into disease dynamics over long time scales. We describe how progress in medicine can be accelerated through the use of sophisticated computational methods for the structured analysis of large multi-patient, multi-signal datasets. We propose two new approaches, morphologic variability (MV) and physiological symbolic analysis, for the analysis of continuous long-term signals. MV studies subtle micro-level variations in the shape of physiological signals over long periods. These variations, which are often widely considered to be noise, can contain important information about the state of the underlying system. Symbolic analysis studies the macro-level information in signals by abstracting them into symbolic sequences. Converting continuous waveforms into symbolic sequences facilitates the development of efficient algorithms to discover high risk patterns and patients who are outliers in a population. We apply our methods to the clinical challenge of identifying patients at high risk of cardiovascular mortality (almost 30% of all deaths worldwide each year). When evaluated on ECG data from over 4,500 patients, high MV was strongly associated with both cardiovascular death and sudden cardiac death. MV was a better predictor of these events than other ECG-based metrics. Furthermore, these results were independent of information in echocardiography, clinical characteristics, and biomarkers.

Department dc:contributor.department

Harvard University--MIT Division of Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Syed, Zeeshan Hassan, 1980-

Advisor dc:contributor.advisor

• John V. Guttag.

• Harvard University--MIT Division of Health Sciences and Technology.
• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/54671

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/54671