Statistics and Its Interface

Volume 5 (2012)

Number 4

Predicting acute hypotensive episodes from ambulatory blood pressure telemetry

Pages: 425 – 429



Kun Jin (Division of Biometrics I, U.S. Food and Drug Administration, Silver Spring, Maryland, U.S.A.)

Norman Stockbridge (Division of Cardiovascular and Renal Products, U.S. Food and Drug Administration, Silver Spring, Maryland, U.S.A.)


The biological data collected from intensive care units contain signal and noise. To extract information that will be useful for predicting or discriminating the cases likely to develop an acute hypotensive episode (AHE), we begin by applying a spline-based smoothing method to the observed mean arterial pressure (MAP) curves. The coefficients of the fitted spline model form a discretization matrix of the continuous MAP curves. A rank-based discriminant analysis and a cross-validation method are developed to find the best prediction subset in the training set. The selected best subsets are used to predict AHE in the test sets. This work is from participation of PhysioNet/Computers in Cardiology Challenge 2009: Predicting Acute Hypotensive Episodes.


B-spline smoothing, rank analysis, cross validation, acute hypertensive episode, ambulatory blood pressure telemetry

2010 Mathematics Subject Classification

Primary 62-07, 62P10. Secondary 65D07, 65D10.

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