A methodology for prediction of acute hypotensive episodes in ICU via a risk scoring model including analysis of ST-segment variations.

The aim of this study is to detect Acute Hypotensive Episodes (AHE) and Mean Arterial Pressure Dropping Regimes (MAPDRs) using ECG signal and Arterial Blood Pressure waveforms. To meet this end, the QRS complexes and end-systolic end-diastolic pulses are first extracted using two innovative Modified Hilbert Transform-Based algorithms namely as ECGMHT and BPMHT. A new smoothing algorithm is next developed based on piecewise polynomial fitting to smooth the fast fluctuations observed in RR-tachogram, systolic blood pressure (SBP) and diastolic blood pressure (DBP) trends. Afterwards, in order to consider the mutual influence of parameters on the evaluation of shock probability, a Sugeno Adaptive Network-based Fuzzy Inference System-ANFIS is trained using Hasdai et al. (J Am Coll Cardiol, 35: 136–143, 2000) parameters as input, with appropriate membership functions for each parameter. Using this network, it will be possible to incorporate the possible mutual influences between risk parameters such as heart rate, SBP, DBP, ST-segment episodes, age, gender, weight and some miscellaneous factors to the calculation of shock occurrence probability. In the next step, the proposed algorithm is applied to 15 subjects of the MIMIC II Database and AHE and MAPDRs (MAP ≤ 60 mmHg with a period of 30 min or more) are identified. As a result of this study, for a sequence of MAPDRs as long as 20 min or more, there will exist a consequent high peak with the duration of 3–4 min in the corresponding probability of cardiogenic shock diagram.