In this study, a new pattern discrimination method for the classication of Mean Arterial
Pressure (MAP) regimes in ICU via an appropriately regulated Radial Basis Function (RBF) Support
Vector Machine (SVM) is described. The aim of this classication is to detect hazardous cardiogenic
shock situations to prevent probable fatal failure of organs. To this end, rst, electrocardiogram (ECG)
and Blood Pressure (BP) waveforms are processed via a Modied Hilbert Transform (MHT), and QRS
complexes (equivalently obtaining heart rate-HR trend) and pressure pulses (equivalently obtaining trends
of systolic, diastolic and mean arterial pressures) are detected, respectively. In the next step, a RBFSVM
classier is tuned using features obtained from the cardiogenic shock risk scoring model developed by
Hasdai et al. (2000) to classify MAP regimes into three categories; survival (the status that will not fall
into shock), critical (the transient status that may lead to shock or a return to the survival episode) and
Acute Hypotensive Episode -AHE (meaning cardiogenic shock will certainly occur.) Then, the regulated
RBF-SVM classier is applied to 60 records of the Computers in Cardiology (CinC) Challenge 2009 and
the values of Se = 92% and P+ = 93% are obtained for sensitivity and positive predictivity, respectively.
As some results of this study, the proposed classication method recognized truly 15 subjects out of 15
normal (without shock episodes) subjects of the MIMICII database as belonging to the survival class",
while the algorithm could classify 24 subjects as AHE", 3 subjects as of the critical class" and 3 subjects
as in the survival" situation out of 30 shock containing records of the MIMICII database.