Filtering of Arterial Blood Pressure Signal Artifact using the Extended Kalman Filter
The figure above depicts several seconds of raw arterial blood pressure (ABP) data obtained from a patient within the MIMIC II physiologic waveform database. [1,2]
This figure shows a raw signal with a tracking signal based on the extended Kalman filter (EKF) overlaid. In this case, the signal error and the process noise are very small (signal noise 0.1 mmHg, process noise 0.5 mmHg). With these settings, the filter tracks the actual signal very closely, and makes it appear as if there is not difference between signal measurement and track.
The full analysis is available at the following link in PDF form:
 Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals.Circulation101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215]; 2000 (June 13).
“Hospital staff are exposed to an average of 350 alarms per bed per day, based on a sample from an intensive care unit at the Johns Hopkins Hospital in Baltimore.”
From the same survey, almost 9 in 10 hospitals indicated they would increase their use of patient monitoring, particularly of Capnography and pulse oximetry, if false alarms could be reduced. 
“Of those hospitals surveyed that monitor some or all patients with pulse oximetry or Capnography, more than 65 percent have experienced positive results in terms of either a reduction in overall adverse events or in reduction of costs.”
Attenuating Alarm Signals
The problem with attenuating alarm data is achieving the balance between communicating the essential, patient-safety specific information that will provide proper notification to clinical staff while minimizing the excess, spurious and non-emergent events that are not indicative of a threat to patient safety. In the absence of contextual information, the option is usually to err on the side of excess because the risk of missing an emergent alarm or notification carries with it the potential for high cost (e.g.: patient harm or death).
The purpose of this study is to look at the and some of the Mathematical Techniques for Mitigating Alarm Fatigue: techniques and options available for evaluating real-time data. The objective is to suggest a dialog for further research and investigation into the use of such techniques as appropriate. Clearly, patient safety, regulatory, staff fatigue and other factors must be taken into account in terms of aligning on a best approach or practice (if one can even be identified). These aspects of alarm fatigue are intentionally omitted from the discussion at this point (to be taken up at another time) so that a pure study of the physics of the parameter data and techniques for analyzing can be explored.
 Ilene MacDonald, “Hospitals rank alarm fatigue as top patient safety concern”, Fierce Healthcare. January 22, 2014.
 Wong, Michael; Mabuyi, Anuj; Gonzalez, Beverly; “First National Survey of Patient-Controlled Analgesia Practices.” March-April 2013, A Promise to Amanda Foundation and the Physician-Patient Alliance for Health & Safety.