DOI QR코드

DOI QR Code

PhysioCover: Recovering the Missing Values in Physiological Data of Intensive Care Units

Kim, Sun-Hee;Yang, Hyung-Jeong;Kim, Soo-Hyung;Lee, Guee-Sang

  • 투고 : 2013.12.23
  • 심사 : 2014.05.02
  • 발행 : 2014.06.28

초록

Physiological signals provide important clues in the diagnosis and prediction of disease. Analyzing these signals is important in health and medicine. In particular, data preprocessing for physiological signal analysis is a vital issue because missing values, noise, and outliers may degrade the analysis performance. In this paper, we propose PhysioCover, a system that can recover missing values of physiological signals that were monitored in real time. PhysioCover integrates a gradual method and EM-based Principle Component Analysis (PCA). This approach can (1) more readily recover long- and short-term missing data than existing methods, such as traditional EM-based PCA, linear interpolation, 5-average and Missing Value Singular Value Decomposition (MSVD), (2) more effectively detect hidden variables than PCA and Independent component analysis (ICA), and (3) offer fast computation time through real-time processing. Experimental results with the physiological data of an intensive care unit show that the proposed method assigns more accurate missing values than previous methods.

키워드

Intensive Care Unit;Missing Values;Hidden variable;Real Time Processing and EM-Principle Component Analysis

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피인용 문헌

  1. Machine Learning and Decision Support in Critical Care vol.104, pp.2, 2016, https://doi.org/10.5392/IJoC.2014.10.2.047