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Publication Type : Conference Paper
Thematic Areas : Wireless Network and Application
Publisher : 2020 IEEE Region 10 Symposium (TENSYMP).
Source : 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1652-1655. IEEE, 2020.
Campus : Amritapuri
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)
Department : Wireless Networks and Applications (AWNA)
Verified : Yes
Year : 2020
Abstract : Blood pressure (BP) is an important vital sign that needs to be monitored regularly to maintain a healthy life. A normal blood pressure is crucial for life and a consistent variation in that can lead to critical health conditions such as kidney failure, cerebral infarction, hypertension and cardiovascular diseases which can be fatal. Hypertension is one of the major reasons for premature death worldwide. An effective non-obtrusive mechanism for continuous monitoring of BP is necessary for the early detection and prevention of fatal events. In this paper, we present the design, development and validation of a neural network based computational model for continuous BP monitoring using photoplethysmograph (PPG) data from the University of Guilin.
Cite this Research Publication : MP, Akshay Krishna, P. Durga, and Rahul Krishnan Pathinarupothi. "Development of a Neural Network based model for Non-obtrusive Computation of BP from Photoplethysmograph." In 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1652-1655. IEEE, 2020.