Consensus motifs as adaptive and efficient predictors for acute hypotensive episodes

Acute hypotensive episodes (AHE) are characterized by continuously low blood pressure for prolonged time, and could be potentially fatal. We present a novel AHE detection system, by first quantizing the blood pressure data into clinically accepted severity ranges and then identifying most frequently...

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Bibliographic Details
Main Authors: Pathinarupothi, Rahul Krishnan, Rangan, Ekanath Srihari
Format: Conference Proceeding
Language:English
Subjects:
Acute hypotensive episodes
Biomedical monitoring
Blood pressure
Consensus motifs
Medical services
Monitoring
Predictive health
Sensors
Support vector machines
Time series analysis
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Summary:Acute hypotensive episodes (AHE) are characterized by continuously low blood pressure for prolonged time, and could be potentially fatal. We present a novel AHE detection system, by first quantizing the blood pressure data into clinically accepted severity ranges and then identifying most frequently occurring blood pressure pattern among these which we call consensus motifs. We apply machine learning techniques (support vector machine) on these consensus motifs. The results show that the use of consensus motifs instead of raw time series data extends the predictability by 45 minutes beyond the 2 hours that is possible using only the raw data, yielding a significant improvement without compromising the clinical accuracy. The system has been implemented as part of a new framework called RASPRO (Rapid Summarization for Effective Prognosis) that we have developed for Wireless Remote Health Monitoring.
ISSN:1557-170X
2694-0604
DOI:10.1109/EMBC.2017.8037166