Publication Type : Conference Paper
Thematic Areas : Wireless Network and Application
Publisher : IEEE EMBC , 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju Island, Korea .
Source : IEEE EMBC , 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju Island, Korea (2017)
Campus : Kochi
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)
Department : Wireless Networks and Applications (AWNA)
Year : 2017
Abstract : 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.
Cite this Research Publication : Rahul K Pathinarupothi and Ekanath Srihari Rangan, “Consensus Motifs as Adaptive and Efficient Predictors for Acute Hypotensive Episodes”, in IEEE EMBC , 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju Island, Korea, 2017.