Elimination of Multicollinearity in Continuous Non-Invasive Blood Pressure Approximation by Information Criterion
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Published:2021-01-01
Issue:1
Volume:11
Page:53-62
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ISSN:2156-7018
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Container-title:Journal of Medical Imaging and Health Informatics
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language:en
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Short-container-title:j med imaging hlth inform
Author:
Bose S. Sree Niranjanaa,Kandaswamy A.
Abstract
Continuous and unobtrusive method of measuring blood pressure has been gaining more attention in the healthcare community. With the application of data analysis techniques in biosignals like Electrocardiogram (ECG) and Photoplethysmogram (PPG), several predictors are obtained that correlates
well with the blood pressure. But the BP approximation regression models formed using these predictors suffers from multicollinearity (higher correlation between predictors). The article proposes the use of information criterion-based model ensemble approach to reduce the effect of multicollinearity
in the continuous BP estimation. The study focuses on forming pool of candidate models from feature subsets. The best performing models are selected based on information criterion and combined to form the ensemble model. Experiments with performed with MIMIC-II dataset that consists of 104
records with simultaneously recorded PPG and arterial BP. The results suggest that the technique achieves Mean Absolute Error (MAE) of 5.81 mm Hg and 3.35 mm Hg for systolic and diastolic BP and Root Mean Square Error (RMSE) of 6.08 mm Hg and 4.12 mm Hg for systolic and diastolic BP respectively.
The error measures conform to the standards set by American Association of Medical Instrumentation (AAMI). The method reveals that the ensemble model based on information criterion outperforms well compared to the usage of single model.
Publisher
American Scientific Publishers
Subject
Health Informatics,Radiology Nuclear Medicine and imaging