Algorithm to identify components of arterial blood pressure signals during use of an intra-aortic balloon pump

Existing bedside cardiovascular monitors often inaccurately measure arterial blood pressure during intra-aortic balloon pump (IABP) assist. We have developed an algorithm that correctly identifies features of arterial pressure waveforms in the presence of IABP. The algorithm is adaptive, functions in real-time, and uses information from the electrocardiographic (ECG) and arterial blood pressure signals to extract features and numeric values from the arterial blood pressure waveform. In its current form, it requires reliable ECG beat detection and was not intended to operate under conditions of extremely poor balloon timing. The algorithm was evaluated by an expert (P.F-C.) on a limited data set, which consisted of 12 1-minute epochs of data recorded from 6 intensive care unit patients. A criterion for selection of patients was that the ECG beat detector could detect ECG beats correctly from the waveforms. The overall sensitivity and positive predictivity for beat detection were 94.04% and 100%, respectively. For feature identification, the overall sensitivity was greater than 89%, positive predictivity was 100%, and the false-positive rate was 0%. The performance measures may be biased by the criteria for patient selection. This approach to identifying waveform features during IABP improves the accuracy of measurements. The utility of using 2 sources of information to improve measurement accuracy has been demonstrated and should be applicable to other physiologic signal-processing applications.