Parallel processing of ECG and blood pressure waveforms for detection of acute hypotensive episodes: a simulation study using a risk scoring model

Abstract

The aim of this study is to detect acute hypotensive episodes (AHE) and mean arterial pressure dropping regimes (MAPDRs) using electrocardiographic (ECG) signals 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 ECGMHT and BPMHT. The resulting systolic and diastolic blood pressure pulses are then used to calculate the MAP trend. A new smoothing algorithm is developed, next based on piecewise polynomial fitting (PPF) to smooth the fast fluctuations observed in RR-tachogram and MAP trends. PPF algorithm operates by sequentially fitting N number of polynomials to the original signal and calculating the corresponding coefficients using the best linear unbiased estimation approach. 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, MAPDR is realised as a specific marker of cardiogenic shock, in that for a sequence of MAPDRs as long as 20 min or more, there will exist a consequent high peak with a duration of 3–4 min in the corresponding probability of cardiogenic shock diagram.