Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/135866
Title: A STUDY OF MACHINE LEARNING APPLICATION IN BLOOD PRESSURE MEASUREMENT
Authors: LI YUNMING
Keywords: blood pressure, machine learning
Issue Date: 29-Dec-2016
Source: LI YUNMING (2016-12-29). A STUDY OF MACHINE LEARNING APPLICATION IN BLOOD PRESSURE MEASUREMENT. ScholarBank@NUS Repository.
Abstract: Effective continuous blood pressure monitoring is essential to preventing cardiovascular diseases. Pulse transit time (PTT) based blood pressure measurement method suffers from maintaining accuracy and re-calibration problem. The purpose of this study is to exam the performances of different machine learning methodologies, including linear regression, feed-forward neural network and recurrent neural network, in continuous blood pressure measurement. The experiment data was selected from MIMIC II online databases. Different initial settings of feed-forward neural network and recurrent neural network were tested. The experiment shows that the best error percentage is 7% using recurrent neural network method. Compared to the other machine learning methods, the proposed recurrent neural network structure with 5 hidden layers, 10 hidden neurons in each layer, has the best performance in tracing patient blood pressure among proposed machine learning structures.
URI: http://scholarbank.nus.edu.sg/handle/10635/135866
Appears in Collections:Master's Theses (Open)

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