Intelligent Alarms for Anaesthesia Monitoring Based on a Fuzzy Logic Approach

  • A. Jungk
  • B. Thull
  • G. Rau
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 83)


One of the most important tasks of the anaesthetist is to monitor the patient’s vital signs in order to evaluate the patient’s state, and to control it according to the needs of the surgical procedure. To support the anaesthetists’ decision making process sensor techniques have been continuously developed by the medical industry. Hence, an increasing large number of vital parameters (e.g.: blood pressures, EEG, ECG, inspired and expired gas fractions etc.) are nowadays displayed by modern monitoring devices especially during highly invasive surgery [1–3]. As a result of this development, over 95% of anaesthesia based critical incidents could be theoretically detected only with the help of a monitor (over 65 % without any organ damage) [4] . Obviously, these new measurement techniques have improved the patient’s safety during the surgical procedure significantly.


Fuzzy Rule Vital Parameter Alarm System Fuzzy Logic Approach Temporal Abstraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    List WF, Metzler H, Pasch T (1995) Monitoring in Anästhesie und Intensivmedizin. Springer, Berlin, HeidelbergGoogle Scholar
  2. [2]
    Petry A (1995) On-line Aufzeichung von Monitordaten. Anaesthesist 44: 818–825PubMedCrossRefGoogle Scholar
  3. [3]
    Martin E (1997) Facharztlehrbuch Anästhesiologie. Blackwell Wissenschaftsverlag, Berlin, WienGoogle Scholar
  4. [4]
    Webb RK, van der Valt JH, Runciman WB, Williamson JA, Cockings J, Russell WJ, Helps S (1993) Which Monitor? An Analysis of 2000 Incident Reports. Anaesthesia Intensive Care 21: 529–542Google Scholar
  5. [5]
    Gaba DM (1991) Human performance issues in anesthesia patient safety. Problems in Anesthesia 5: 329–350Google Scholar
  6. [6]
    Waterson C, Calkins JM (1986) Development directions for monitoring in anesthesia. Seminars in Anesthesia V(3): 225–236Google Scholar
  7. [7]
    Coiera E (1993) Intelligent monitoring and control of dynamic physiological systems. Artificial Intelligence in Medicine 5: 1–8PubMedCrossRefGoogle Scholar
  8. [8]
    Chopra V, Bovill JG, Spierdijk J, Koornneef F (1992) Reported significant observations during anesthesia: a prospective analysis over a 18-month period. British Journal of Anesthesia 68: 13–17CrossRefGoogle Scholar
  9. [9]
    Chopra V, Bovill JG, Spierdijk J (1990) Accidents, near accidents and complications during anesthesia: A retrospective analysis of a 10-year period in a teaching hospital. Anesthesia 45: 3–6CrossRefGoogle Scholar
  10. [10]
    Short TG, O’Regan A, Lew J, Oh TE (1992) Critical incident reporting in an anaesthetic department quality assurance programme. Anaesthesia 47: 3–7CrossRefGoogle Scholar
  11. [11]
    Cooper JB, Newbower RS, Kitz RJ (1984) An analysis of major errors and equipment failures in anaesthesia management: Considerations for prevention and detection. Anesthesiology 60: 34–42PubMedCrossRefGoogle Scholar
  12. [12]
    Webb RK, Currie M, Morgan CA, Williamson JA, Mackay P, Russell WJ, Runciman WB (1993) The australian incident monitoring study: An analysis of 2000 incident reports. Anaesthesia Intensive Care 21(5): 506–519Google Scholar
  13. [13]
    Boquet G, Bushman JA, Davenport H (1980) The anaesthetic machine- a study of function and design. British Journal of Anaesthesia 52: 61–67PubMedCrossRefGoogle Scholar
  14. [14]
    Weinger MB, Herndon OW, Zornow MH, Paulus MP, Gaba DM, Dallen LT (1994) An objective methodology for task analysis and workload assessment in anesthesia providers. Anesthesiology 80: 77–92PubMedCrossRefGoogle Scholar
  15. [15]
    Runciman WB, Sellen A, Webb RK, Williamson JA, Currie M, Morgan C, Russell (1993) Errors, Incidents and Accidents in Anaesthetic Practice. Anaesthesia Intensive Care 21: 506–519Google Scholar
  16. [16]
    Block E, Nuutinen L, Ballast B (1999) Optimization of alarms: a study on alarm limits, alarm sounds, and false alarms, intended to reduce annoyance. Journal of Clinical Monitoring and Computing 15: 75–83PubMedCrossRefGoogle Scholar
  17. [17]
    Weinger MB, Englund CE (1990) Ergonomic and Human Factors Affecting Anesthetic Vigilance and Monitoring Performance in the Operating Room Environment. Anesthesiology 73: 995–1021PubMedCrossRefGoogle Scholar
  18. [18]
    Cook RI, Block FE, McDonald JS (1988) Cascade of Monitor Detection of Anesthetic Disaster. Anesthesiology 59(3A): A277Google Scholar
  19. [19]
    Becker K, Thull B, Käsmacher-Leidinger H, Stemmer J, Rau G, Kalff G, Zimmermann H-J (1997) Design and validation of an intelligent alarm system based on a fuzzy logic process model. Artificial Intelligence in Medicine 11: 33–53PubMedCrossRefGoogle Scholar
  20. [20]
    Schecke T, Rau G, Popp H-J, Käsmacher H, Kalff G, Zimmermann H-J (1991) A Knowledge-Based Approach to Intelligent Alarms in Anesthesia. IEEE Engineering in Medicine and Biology 10(4): 38–43CrossRefGoogle Scholar
  21. [21]
    Vicente K, Rasmussen J (1992) Ecological interface design: theoretical foundations. IEEE Trans. System, Man, and Cybernetics 22(4): 589–606CrossRefGoogle Scholar
  22. [22]
    Uckun S (1994) Intelligent systems in patient monitoring and therapy measurement: A survey of research projects. International Journal of Clinical Monitoring and Computing 11: 241–253PubMedCrossRefGoogle Scholar
  23. [23]
    Mora FA, Passariello G, Carrault G, Le Pichon J-P (1993) Intelligent patient monitoring and management systems. IEEE Engineering in Medicine and Biology December: 23–33Google Scholar
  24. [24]
    Adlassnig K-P (1982). A survey on medical diagnosis and fuzzy subsets. In: Gupta MM, Sanchez E (eds.): Approximate Reasoning in Decision Analysis, North-Holland, New York, pp 203–217Google Scholar
  25. [25]
    Guez A, Nevo I (1996) Neural networks and fuzzy logic in clinical laboratory computing with application to integrated monitoring. Clinica Chimica Acta 248: 73–90CrossRefGoogle Scholar
  26. [26]
    de Graaf PMA, van den Eijkel GC, Vullings HJLM, de Mol BAJM (1997) A decision-driven design of a decision support system in anesthesia. Artificial Intelligence in Medicine 11(2): 141–153PubMedCrossRefGoogle Scholar
  27. [27]
    Lowe A, Jones RW, Harrison MJ (1999) Temporal Pattern Matching Using Fuzzy Templates. Journal of Intelligent Information Systems 13: 27–45CrossRefGoogle Scholar
  28. [28]
    Lowe A, Harrison MJ, Jones RW (1999) Diagnostic monitoring in anaesthesia using fuzzy trend templates for matching temporal patterns. Artificial Intelligence in Medicine 16, 183–199PubMedCrossRefGoogle Scholar
  29. [29]
    Shieh JS, Linkens DA, Peacock JE (1999) Hierarchical Rule-Based and SelfOrganizing Fuzzy Logic Control for Depth of Anaesthesia. IEEE Trans. on Systems, Man, and Cybernetics Part C 29(1): 98–109CrossRefGoogle Scholar
  30. [30]
    Oberli C, Urzua J, Saez C, Guarini M, Cipriano A, Garayar B, Lema G, Canessa R, Sacco C, Irirrazaval M (1999) An expert system for monitor alarm integration. Journal of Clinical Monitoring and Computing 15: 29–35PubMedCrossRefGoogle Scholar
  31. [31]
    Vila J, Presedo J, Delgado M, Barro S, Ruiz R, Palacios F (1997) SUTIL: Intelligent ischemia monitoring system. International Journal of Medical Informatics 47: 193–214PubMedCrossRefGoogle Scholar
  32. [32]
    Steimann, F (1996) The interpretation of time-varying data with DiaMon-1. Artificial intelligence in medicine 8: 343–357PubMedCrossRefGoogle Scholar
  33. [33]
    Larsson JE, Hayes-Roth B, Gaba DM, Smith BE (1997) Evaluation of a medical diagnosis system using simulator test scenarios. Artificial Intelligence in Medicine 11: 119–140PubMedCrossRefGoogle Scholar
  34. [34]
    Drakopoulos JA, Hayes-Roth B (1998) tFPR: A fuzzy and structural pattern recognition system of multi-variate time-dependent pattern classes based on sigmoidal functions. Fuzzy Sets and Systems 99: 57–72CrossRefGoogle Scholar
  35. [35]
    Larizza C, Bernuzzi G, Stefanelli M (1995). A General Framework for Building Patient Monitoring Systems. In: Barahona P, Stefanelli M, Wyatt J (eds.): Lecture Notes in Artificial Intelligence, Springer Verlag, Berlin, pp 91–102Google Scholar
  36. [36]
    Sittig DF, Factor M (1990) Physiologic trend detection and artifact rejection: a parallel implementation of a multi-state Kalman filtering algorithm. Computer Methods and Programs in Biomedicine 31: 1–10PubMedCrossRefGoogle Scholar
  37. [37]
    Miksch S, Horn W, Popow C, Paky F (1993). VIE-VENT: Knowledge-Based Monitoring and Therapy Planning of the Artificial Ventilation of Newborn Infants. In: Andreassen et al. (eds.): Artificial Intelligence in Medicine, IOS Press, Amsterdam, pp 218–229Google Scholar
  38. [38]
    Miksch S, Horn W, Popow C, Paky F (1995). Therapy Planning Using Qualitative Trend Descriptions. In: Barahona P, Stefanelli M, Wyatt J (eds.): Lecture Notes in Artificial Intelligence, Springer Verlag, Berlin, pp 197–208Google Scholar
  39. [39]
    Horn W, Miksch S, Egghart G, Popow C, Paky F (1997) Effective data validation of high-frequency data: time-point-, time-interval-, and trend-based methods. Comput Biol Med 27(5): 389–409PubMedCrossRefGoogle Scholar
  40. [40]
    Westenskow DR, Orr JA, Simon FH, Bender H-J, Frankenberger H (1992) Intelligent Alarms Reduce Anesthesiologist’s Response Time to Critical Faults. Anesthesiology 77 : 1074–1079PubMedCrossRefGoogle Scholar
  41. [41]
    Narus SP, Kück K, Westenskow DR (1995). Intelligent Monitor for an Anesthesia Breathing Circuit. In: Proc Annu Symp Comput Appl Med Care, AMIA Inc., pp 96–100Google Scholar
  42. [42]
    Sukuvaara T, Koski EMJ, Mäkivirta A, Kari A (1993) A knowledge-based alarm system for monitoring cardiac operated patients — technical construction and evaluation. International J. of Clin. Monitoring and Computing 10: 117–126CrossRefGoogle Scholar
  43. [43]
    Sukuvaara T, Sydänmaa M, Nieminen H, Heikelä A, Koski EMJ (1993) Object-Oriented Implementation of an Architecture for Patient Monitoring. IEEE Engineering in Medicine and Biology December: 69–81Google Scholar
  44. [44]
    Koski EMJ, Sukuvaara T, Mäkivirta A, Kari A (1994) A knowledge-based system for monitoring cardiac operated patients — assessment of clinical performance. International J. of Clin. Monitoring and Computing 11: 79–83CrossRefGoogle Scholar
  45. [45]
    Haimowitz IJ, Le PP, Kohane IS (1995) Clinical monitoring using regressionbased trend templates. Artificial Intelligence in Medicine 7: 473–496PubMedCrossRefGoogle Scholar
  46. [46]
    Shahar Y, Musen MA (1996) Knowledge-based temporal abstraction in clinical domains. Artificial Intelligence in Medicine 8: 267–298PubMedCrossRefGoogle Scholar
  47. [47]
    Dawant BM, Uckun S, Manders EJ, Lindstrom DP (1993) The SIMON Project: Model-Based Signal Acquisition, Analysis and Interpretation In Intelligent Patient Monitoring. IEEE Engineering in Medicine and Biology December: 82–91Google Scholar
  48. [48]
    Coiera E (1994) Monitoring in Anaesthesia and Intensive Care. W.B. Sounders, LondonGoogle Scholar
  49. [49]
    Salatin A, Hunter J (1999) Deriving trends in historical and real-time continously sampled medical data. Journal of Intelligent Information Systems 13: 47–71CrossRefGoogle Scholar
  50. [50]
    Bronstein IN, Semendjajew KA (1989) Taschenbuch der Mathematik. Teubner Verlag, LeipzigGoogle Scholar
  51. [51]
    Becker K (1996) Der Einsatz quantitativer und qualitativer Methoden bei der Implementierung und Validierung eines intelligenten Entscheidungsunterstützungs- und Alarmsystems für die Kardioanästhesie. Dissertation, RWTH AachenGoogle Scholar
  52. [52]
    Larsen R (1985) Anästhesie. Urban&Schwarzenberg Verlag, MünchenGoogle Scholar
  53. [53]
    Nemes C, Niemer M, Noack G (1982) Datenbuch Anästhesiologie. Gustav Fischer Verlag, StuttgartGoogle Scholar
  54. [54]
    Jungk A, Thull B, Rau G (1999). Evaluation of an ecological interface for the anaesthesia workplace by eye-tracking. In: Bullinger H-J, Vossen PH (eds): Adjunct Proc. of the 8th HCI International ’99, Fraunhofer IRB Verlag, Stuttgart, pp 31–32Google Scholar
  55. [55]
    Kosko B (1992) Neural Networks and Fuzzy Systems. Prentice Hall International, Englewood-CliffsGoogle Scholar
  56. [56]
    Zimmermann H-J (1996) Fuzzy Set Theory and its Applications. 2nd ed., Kluwer, DordrechtGoogle Scholar
  57. [57]
    Zimmermann H-J (1993) Fuzzy-Technologien: Prinzipien, Werkzeuge, Potentiale. VDI Verlag, DüsseldorfGoogle Scholar
  58. [58]
    Jungk A, Thull B, Hoeft A, Rau G (2000) Ergonomic Evaluation of an Ecological Interface and a Profilogram Display for Hemodynamic Monitoring. Journal of Clinical Monitoring and Computing (in press)Google Scholar
  59. [59]
    Rasmussen J, Mark Pejtersen A, Goodstein LP (1994) Cognitive System Engineering. John Wiley, New YorkGoogle Scholar
  60. [60]
    Gravenstein JS, Paulus DA (1985) Praxis der Patientenüberwachung. Fisher Verlag, StuttgartGoogle Scholar
  61. [61]
    Michels P, Gravenstein D, Westenskow DR (1997) An integrated graphic data display improves detection and identification of critical events during anesthesia. Journal of Clinical Monitoring 13(4): 249–259PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • A. Jungk
    • 1
  • B. Thull
    • 2
  • G. Rau
    • 1
  1. 1.Ergonomics in Medicine Helmholtz-Institute for Biomedical EngineeringAachen University of Technology (RWTH)AachenGermany
  2. 2.Department of Information and DesignUniversity of Applied ScienceDarmstadtGermany

Personalised recommendations