Reducing the computational demands of medical monitoring classifiers by examining less data

Instrumenting patients with small, wearable sensors will enable physicians to continuously monitor patients outside the hospital. These devices can be used for real-time classification of the data they collect. For practical purposes, such devices must be comfortable and thus be powered by small batteries. Since classification algorithms often perform energy-intensive signal analysis, power management techniques are needed to achieve reasonable battery lifetimes. In this thesis, we describe software-based methods that reduce the computation, and thus, energy consumption of real-time medical monitoring algorithms by examining less data. Though discarding data can degrade classification performance, we show that the degradation can be small. We describe and evaluate data reduction methods based on duty cycling, sensor selection, and combinations of the two. Random duty cycling was applied to an online algorithm that performs risk assessment of patients with a recent acute coronary syndrome (ACS). We modified an existing algorithm that estimates the risk of cardiovascular death following ACS. By randomly discarding roughly 40% of the data, we reduced energy consumption by 40%. The percentage of patients who had a change in their risk classification was 3%. A sensor selection method was used to modify an existing machine learning based algorithm for constructing multi-channel, patient-specific, delay-sensitive seizure onset detectors.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Shih, Eugene Inghaw, 1976-

Advisor dc:contributor.advisor

• John V. Guttag.

• 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/1721.1/57681

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/57681