Alarm algorithms in critical monitoring

Anesthesia and Analgesia

Volumn 102, Issue 5, 2006, Pages 1525-1537

  Imhoff, Michael ^a,c   Kuhls, Silvia ^b  

^a RUHR UNIVERSITY BOCHUM   (Germany)

^b TU DORTMUND UNIVERSITY   (Germany)

^c NONE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALARM MONITOR; ALGORITHM; CAREGIVER; DEVICE; HUMAN; INTENSIVE CARE; INTENSIVE CARE UNIT; MATHEMATICAL COMPUTING; PATIENT CARE; PATIENT MONITORING; PATIENT SAFETY; PERIOPERATIVE PERIOD; PRIORITY JOURNAL; REVIEW; STATISTICAL ANALYSIS; TECHNOLOGY; VENTILATOR;

EID: 33646187603     PISSN: 00032999     EISSN: None     Source Type: Journal    
DOI: 10.1213/01.ane.0000204385.01983.61     Document Type: Article

References (70)

1. Chambrin MC, Ravaux P, Calvelo-Aros D, et al. Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med 1999;25:1360-6.
2. O'Carroll TM. Survey of alarms in an intensive therapy unit. Anaesthesia 1986;41:742-4.
3. Lawless ST. Crying wolf: false alarms in a pediatric intensive care unit. Critical Care Med 1994;22:981-5.
4. Koski EM, Mäkivirta A, Sukuvaara T, Kari A. Frequency and reliability of alarms in the monitoring of cardiac postoperative patients. Int J Clin Monit Comput 1990;7:129-33.
5. Tsien CL, Fackler JC. Poor prognosis for existing monitors in the intensive care unit. Crit Care Med 1997;25:614-9.
6. Biot L, Carry PY, Perdrix JP, et al. Evaluation clinique de la pertinence des alarmes en réanimation. Ann Fr Réanim 2000;19:459-66.
7. Meredith C, Edworthy J. Are there too many alarms in the intensive care unit? An overview of the problems. J Adv Nurs 1995;21:15-20.
8. Koski EM, Mäkivirta A, Sukuvaara T, Kari A. Clinicians' opinions on alarm limits and urgency of therapeutic responses. Int J Clin Monit Comput 1995;12:85-8.
9. Cropp AJ, Woods LA, Raney D, Bredle DL. Name that tone: the proliferation of alarms in the intensive care unit. Chest 1994;105:1217-20.
10. Momtahan K, Retu R, Tansley B. Audibility and identification of auditory alarms in the operating room and intensive care unit. Ergonomics 1993;36:1159-76.
11. Fried R, Gather U, Imhoff M, Bauer M. Statistical methods in intensive care online monitoring, 2000. Technical Report 33/00, SFB 475. University of Dortmund. Available from http://www.sfb475.uni-dortmund.de/berichte/tr33-00.ps.
12. Sukuvaara T, Koski EM, Mäkivirta A, Kari A. A knowledge-based alarm system for monitoring cardiac operated patients-technical construction and evaluation. Int J Clin Monit Comput 1993;10:117-26.
13. Davies PL, Fried R, Gather U. Robust signal extraction for on-line monitoring data. J Stat Plan Infer 2003;122:65-78.
14. Siegel AF. Robust regression using repeated medians. Biometrika 1982;68:242-4.
15. Fried R. Robust filtering of time series with trends. J Non-parametr Stat 2004;16:313-28 (Special Issue).
16. Gather U, Schettlinger K, Fried R. Online signal extraction by robust linear regression. Computational Statistics. In press.
17. Bernholt T, Fried R. Computing the update of the repeated median regression line in linear time. Inform Process Lett 2003;88:111-7.
18. Charbonnier S, Becq G, Biot L. On-line segmentation algorithm for continuously monitored data in intensive care units. IEEE Trans Biomed Engl 2004;51:484-92.
19. Mäkivirta A, Koski E, Kari A, Sukuvaara T. The median filter as a preprocessor for a patient monitor limit alarm system in intensive care. Comput Methods Programs Biomed 1991;34:139-44.
20. Shewhart WA. Economic control of quality manufactured product. Princeton, NJ: D. Van Nostrand Reinhold, 1931.
21. Trigg DW. Monitoring a forecasting system. Operat Res Q 1964;15:271-4.
22. Kennedy RR. A modified Trigg's tracking variable as an "advisory" alarm during anaesthesia. Int J Clin Monit Comput 1995;12:197-204.
23. Hill DW, Endresen J. Trend recording and forecasting in intensive care therapy. Br J Clin Equipment 1978;5-14.
24. Imhoff M, Bauer M, Gather U, Löhlein D. Statistical pattern detection in univariate time series of intensive care on-line monitoring data. Intensive Care Med 1998;24:1305-14.
25. Imhoff M, Bauer M, Gather U, Löhlein D. Time series analysis in intensive care medicine. Appl Cardiopulmonary Pathophysiol 1997;6:263-81.
26. Smith AFM, West M, Gordon K, et al. Monitoring kidney transplant patients. Statistician 1983;32:46-54.
27. Trimble IM, West M, Knapp MS, et al. Detection of renal allograft rejection by computer. BMJ 1983;286:1695-9.
28. Gordon K, Smith AFM. Modeling and monitoring biomŕdical time series. J Am Stat Assoc 1990;85:328-37.
29. Daumer M, Falk M. Online change point detection (for state space models) using multi-process Kalman filters. Linear Algebra Appl 1998;284:125-35.
30. Imhoff M, Bauer M, Gather U, Fried R. Pattern detection in intensive care monitoring time series with autoregressive models: influence of the model order. Biometrical J 2002;44:746-61.
31. Gather U, Bauer M, Fried R. The identification of multiple outliers in online monitoring data. Estadistica 2002;54:289-338.
32. Gather U, Fried R, Imhoff M. Online classification of states in intensive care. In: Gaul W, Opitz O, Schader M, eds. Data analysis. Berlin: Springer, 2000:413-28.
33. Schoenberg R, Sands DZ, Safran C. Making ICU alarms meaningful: a comparison of traditional vs. trend-based algorithms. Proc AMIA Symp 1999;379-83.
34. Koski EM, Mäkivirta A, Sukuvaara T, Kari A. Development of an expert system for haemodynamic monitoring: computerized symbolization of on-line monitoring data. Int J Clin Monit Comput 1991-92;8:289-93.
35. Fried R, Imhoff M. On the online detection of monotonic trends in time series. Biometrical J 2004;46:90-102.
36. Brillinger DR. Consistent detection of a monotonic trend super-posed by a stationary time series. Biometrika 1989;76:23-30.
37. Haimowitz I, Le PP, Kohane IS. Clinical monitoring using regression-based trend templates. Artif Intell Med 1995;7:473-96.
38. Miller G. The magical number seven, plus or minus two: some limits to our capacity for processing information. Psychol Rev 1956;63:81-97.
39. Gather U, Becker C. The curse of dimensionality-a challenge for mathematical statistics. Jahresbericht der Deutschen Mathematiker-Vereinigung 2001;103:19-36.
40. Dahlhaus R. Graphical interaction models for multivariate time series. Metrika 2000;51:157-72.
41. Gather U, Imhoff M, Fried R. Graphical models for multivariate time series from intensive care monitoring. Stat Med 2000;21:2685-701.
42. Gather U, Fried R, Lanius V, Imhoff M. Online monitoring of high dimensional physiological time series-a case study. Estadistica 2001;3:259-98.
43. Hanson CW, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med 2001;29:427-35.
44. Krol M, Reich DL. Expert systems in anaesthesiology. Drugs Today 1998;34:593-601.
45. Westenskow DR, Orr JA, Simon FH, et al. Intelligent alarms reduce anesthesiologist's response time to critical faults. Anesthesiology 1992;77:1074-9.
46. Koski EM, Sukuvaara T, Mäkivirta A, Kari A. A knowledge-based alarm system for monitoring cardiac operated patients-assessment of clinical performance. Int J Clin Monit Comput 1994;11:79-83.
47. Morris AH. Computerized protocols and beside decision support. Crit Care Clin 1999;15:523-45.
48. Imhoff M, Gather U, Morik K. Development of decision support algorithms for intensive care medicine: a new approach combining time series analysis and a knowledge base system with learning and revision capabilities. In: Burgard, Christaller, Cremers, eds. K1-99, Advances in Artificial Intelligence, 23rd Annual German Conference on Artificial Intelligence. Springer, 1999:219-30.
49. Morik K, Imhoff M, Brockhausen P, et al. Knowledge discovery and knowledge validation in intensive care. Artif Intell Med 2000;19:225-49.
50. Vapnik V. Statistical learning theory. New York: Wiley, 1998.
51. Morik K, Wrobel S, Kietz LU, Emde W. Knowledge acquisition and machine learning-theory, methods and applications. London: Academic Press, 1993.
52. Miksch S, Horn W, Popow C, Paky F. Utilizing temporal abstraction for data validation and therapy planning for artificially ventilated newborn infants. Artif Intell Med 1996;8:543-76.
53. Müller B, Hasman A, Blom JA. Evaluation of automatically learned intelligent alarm systems. Comput Methods Programs Biomed 1997;54:209-26.
54. Talmon JL. A multiclass nonparametric partitioning algorithm. Pattern Recognit Lett 1986;4:31-8.
55. Ulbricht C, Dorffher G, Lee A. Neural networks for recognizing patterns in cardiotocograms. Artif Intell Med 1998;12:271-84.
56. Farrell RM, Orr JA, Kuck K, Westenskow DR. Differential features for a neural network based anesthesia alarm system. Biomed Sci Instrum 1992;28:99-104.
57. Orr JA, Westenskow DR. A breathing circuit alarm system based on neural networks. J Clin Monit 1994;10:101-9.
58. Tsien CL. Event discovery in medical time-series data. Proc AMIA Symp 2000:858-62.
59. Zadeh LA. Fuzzy sets. Inform Control 1965;8:338-53.
60. Bates JHT, Young MP. Applying fuzzy logic to medical decision making in the intensive care unit. Am J Respir Crit Care Med 2003;167:948-52.
61. Becker K, Thull B, Kasmacher-Leidinger H, et al. Design and validation of an intelligent patient monitoring and alarm system based on a fuzzy logic process model. Artif Intell Med 1997;11:33-53.
62. Lowe A, Jones RW, Harrison MJ. A graphical representation of decision support information in an intelligent anaesthesia monitor. Artif Intell Med 2001;22:173-91.
63. Wolf M, Keel M, von Siebenthal K, et al. Improved monitoring of preterm infants by fuzzy logic. Technol Health Care 1996;4:193-201.
64. Oberli C, Urzua J, Saez C, et al. An expert system for monitor alarm integration. J Clin Monit Comput 1999;15:29-35.
65. Zong W, Moody GB, Mark RG. Reduction of false arterial blood pressure alarms using signal quality assessment and relationships between the electrocardiogram and arterial blood pressure. Med Biol Engl Comput 2004;42:698-706.
66. Laursen P. Event detection on patient monitoring data using causal probabilistic networks. Method Inform Med 1994;33:111-5.
67. McIntosh N. Intensive care monitoring: past, present and future. Clin Med 2002;2:349-55.
68. IEC. Medical electrical equipment, Part 1-8: general requirements for safety - Collateral standard: general requirements, tests and guidance for alarm systems in medical electrical equipment and medical electrical systems. IEC/TRF 60601-1-8 - Ed. 1.0, 2004.
69. Medical electrical equipment. Capnometers for use with humans. Particular requirements. BS EN 864, 1997.
70. IEC. Medical electrical equipment, Part 2-54: particular requirements for the basic safety and essential performance of pulse oximeters for medical use. IEC 60601-1-2-Consol. Ed. 2.1, 2004.