In this paper; we present an effective algorithm for detecting the onset of anerial blood pressure (ABP) IntroductionThe ABP waveform contains rich information about the cardiovascular system, such as heart rate, systolic, mean, and diastolic arterial pressures, and it can be used to assess properties of the arterial vessel wall [l, 21. Reliable and accurate ABP pulse detection is crucial for beat-by-beat extraction and analysis of the information mentioned above. This task is rendered difficult, however, since the ABP measurement is prone to noise and artifacts (see Figure 1 (b) and (d)). Furthermore, the waveform morphology can change dramatically, even over short periods of time, in response to altered pathologic or physiologic stresses (Figure 1 (a) and (c)).Although it is the ABP pulse onset that denotes the arrival of the arterial pressure pulse at the recording site, most ABP pulse and pulse-component detection algorithms identify the peak of the ABP waveform as the fiducial

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The paper presents an algorithm for reducing false alarms related to changes in arterial blood pressure (ABP) in intensive care unit (ICU) monitoring. The algorithm assesses the ABP signal quality, analyses the relationship between the electrocardiogram and ABP using a fuzzy logic approach and post-processes (accepts or rejects) ABP alarms produced by a commercial monitor. The algorithm was developed and evaluated using unrelated sets of data from the MIMIC database. By rejecting 98.2% (159 of 162) of the false ABP alarms produced by the monitor using the test set of data, the algorithm was able to reduce the false ABP alarm rate from 26.8% to 0.5% of ABP alarms, while accepting 99.8% (441 of 442) of true ABP alarms. The results show that the algorithm is effective and practical, and its use in future patient monitoring systems is feasible.