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Volumn 24, Issue 5, 2010, Pages 382-408

On-line adaptive trend extraction of multiple physiological signals for alarm filtering in intensive care units

Author keywords

Alarm filtering; Biomedical engineering; Fuzzy decision making; Monitoring; State change indices; Trend analysis

Indexed keywords

ADULT PATIENTS; BACKWARD ANALYSIS; EXTRACTION METHOD; FALSE ALARMS; FIXED THRESHOLD; FUZZY DECISION MAKING; MEDICAL EXPERTS; MONITORING SYSTEM; PATIENT STATE; PHYSIOLOGICAL SIGNALS; SEMI-QUANTITATIVE; SIGNAL VARIATIONS; SYSTEM-BASED; TIME EVOLUTIONS; TREND ANALYSIS;

EID: 77951604031     PISSN: 08906327     EISSN: 10991115     Source Type: Journal    
DOI: 10.1002/acs.1123     Document Type: Article
Times cited : (23)

References (45)
  • 2
    • 0022503076 scopus 로고
    • Survey of alarms in an intensive therapy units
    • O'Carrol T. Survey of alarms in an intensive therapy units. Anaesthesia 1986; 41:742-744.
    • (1986) Anaesthesia , vol.41 , pp. 742-744
    • O'Carrol, T.1
  • 3
    • 0028300877 scopus 로고
    • Crying wolf: False alarms in a paediatric intensive care unit
    • Lawless S. Crying wolf: false alarms in a paediatric intensive care unit. Critical Care Medicine 1994; 22:981-985.
    • (1994) Critical Care Medicine , vol.22 , pp. 981-985
    • Lawless, S.1
  • 4
  • 5
    • 0034902182 scopus 로고    scopus 로고
    • Alarms in the intensive care unit: How can the number of false alarms be reduced?
    • Chambrin MC. Alarms in the intensive care unit: how can the number of false alarms be reduced? Critical Care 2001; 5:184-188.
    • (2001) Critical Care , vol.5 , pp. 184-188
    • Chambrin, M.C.1
  • 6
    • 0030999897 scopus 로고    scopus 로고
    • Poor prognosis for existing monitors in the intensive care unit
    • Tsien C, Fackler J. Poor prognosis for existing monitors in the intensive care unit. Critical Care Medicine 1997; 25(4): 614-619.
    • (1997) Critical Care Medicine , vol.25 , Issue.4 , pp. 614-619
    • Tsien, C.1    Fackler, J.2
  • 7
    • 33646187603 scopus 로고    scopus 로고
    • Alarm algorithms in critical care monitoring
    • Imhoff M, Kulhs S. Alarm algorithms in critical care monitoring. Anesthesia and Analgesia 2006; 102:1525-1537.
    • (2006) Anesthesia and Analgesia , vol.102 , pp. 1525-1537
    • Imhoff, M.1    Kulhs, S.2
  • 9
    • 1242276321 scopus 로고    scopus 로고
    • On-line segmentation algorithm for continuously monitored data in intensive care units
    • Charbonnier S, Becq G, Biot L. On-line segmentation algorithm for continuously monitored data in intensive care units. IEEE Transactions on Biomedical Engineering 2004; 51:484-492.
    • (2004) IEEE Transactions on Biomedical Engineering , vol.51 , pp. 484-492
    • Charbonnier, S.1    Becq, G.2    Biot, L.3
  • 11
    • 33747288242 scopus 로고    scopus 로고
    • On-line signal extraction by robust linear regression
    • Gather U, Schettlinger K, Fried R. On-line signal extraction by robust linear regression. Computational Statistics 2006; 21(1):33-51.
    • (2006) Computational Statistics , vol.21 , Issue.1 , pp. 33-51
    • Gather, U.1    Schettlinger, K.2    Fried, R.3
  • 12
    • 38149022175 scopus 로고    scopus 로고
    • Factorial switching Kalman filters for condition monitoring in neonatal intensive care
    • MIT Press: Cambridge, MA
    • Williams CKI, Quinn JA, Mcintosh N. Factorial switching Kalman filters for condition monitoring in neonatal intensive care. Advances in Neural Information Processing Systems, vol.18. MIT Press: Cambridge, MA, 2006.
    • (2006) Advances in Neural Information Processing Systems , vol.18
    • Williams, C.K.I.1    Quinn, J.A.2    McIntosh, N.3
  • 13
    • 0033238017 scopus 로고    scopus 로고
    • Artifact detection in the PO2 and PCO2 time series monitoring data from preterm infants
    • Cao C, McIntosh N, Kohane IS, Wang K. Artifact detection in the PO2 and PCO2 time series monitoring data from preterm infants. Journal of Clinical Monitoring and Computing 1999; 15(6):369-378.
    • (1999) Journal of Clinical Monitoring and Computing , vol.15 , Issue.6 , pp. 369-378
    • Cao, C.1    McIntosh, N.2    Kohane, I.S.3    Wang, K.4
  • 14
    • 0031647499 scopus 로고    scopus 로고
    • Statistical pattern detection in univariate time series of intensive care online monitoring data
    • Imhoff M, Bauer M, Gather U, Löhlein D. Statistical pattern detection in univariate time series of intensive care online monitoring data. Intensive Care Medicine 1998; 24: 1305-1314.
    • (1998) Intensive Care Medicine , vol.24 , pp. 1305-1314
    • Imhoff, M.1    Bauer, M.2    Gather, U.3    Löhlein, D.4
  • 15
    • 1542312188 scopus 로고    scopus 로고
    • On-line classification of states in intensive care
    • Gaul W, Opitz O, Schader M (eds). Springer: Berlin
    • Gather U, Fried R, Imhoff M. On-line classification of states in intensive care. In Data Analysis, Gaul W, Opitz O, Schader M (eds). Springer: Berlin, 2000; 413-428.
    • (2000) Data Analysis , pp. 413-428
    • Gather, U.1    Fried, R.2    Imhoff, M.3
  • 16
    • 33646168575 scopus 로고    scopus 로고
    • The identification of multiple outliers in on-line monitoring data
    • Gather U, Bauer M, Fried R. The identification of multiple outliers in on-line monitoring data. Estadistica 2002; 54: 289-338.
    • (2002) Estadistica , vol.54 , pp. 289-338
    • Gather, U.1    Bauer, M.2    Fried, R.3
  • 17
    • 0036037557 scopus 로고    scopus 로고
    • Pattern detection in intensive care monitoring time series with autoregressive models: Influence of the model order
    • 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 Journal 2002; 44:746-761.
    • (2002) Biometrical Journal , vol.44 , pp. 746-761
    • Imhoff, M.1    Bauer, M.2    Gather, U.3    Fried, R.4
  • 20
    • 34248158172 scopus 로고    scopus 로고
    • A trend-based alarm system to improve patient monitoring in intensive care units
    • Charbonnier S, Gentil S. A trend-based alarm system to improve patient monitoring in intensive care units. Control Engineering Practice 2007; 15(9):1039-1050.
    • (2007) Control Engineering Practice , vol.15 , Issue.9 , pp. 1039-1050
    • Charbonnier, S.1    Gentil, S.2
  • 22
    • 0034235507 scopus 로고    scopus 로고
    • Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care units
    • Tsien C, Kohane I, McIntosh N. Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care units. Artificial Intelligence in Medicine 2000; 19:189-202.
    • (2000) Artificial Intelligence in Medicine , vol.19 , pp. 189-202
    • Tsien, C.1    Kohane, I.2    McIntosh, N.3
  • 28
    • 0033257339 scopus 로고    scopus 로고
    • Making ICU alarms meaningful: A comparison of traditional vs. trend- based algorithms
    • Washington, DC, U.S.A.
    • Schoenberg R, Sands DZ, Safran C. Making ICU alarms meaningful: a comparison of traditional vs. trend- based algorithms. Proceedings of the AMIA'99 Symposium, Washington, DC, U.S.A., 1999; 379-383.
    • (1999) Proceedings of the AMIA'99 Symposium , pp. 379-383
    • Schoenberg, R.1    Sands, D.Z.2    Safran, C.3
  • 29
    • 0037201034 scopus 로고    scopus 로고
    • Graphical models for multivariate time series from intensive care monitoring
    • Gather U, Imhoff M, Fried R. Graphical models for multivariate time series from intensive care monitoring. Statistics in Medicine 2000; 21(18):2685-2701.
    • (2000) Statistics in Medicine , vol.21 , Issue.18 , pp. 2685-2701
    • Gather, U.1    Imhoff, M.2    Fried, R.3
  • 31
    • 84862219783 scopus 로고    scopus 로고
    • ICU patient state characterization using machine learning in a time series framework
    • Lecture Notes in Artificial Intelligence, Springer: Berlin
    • Calvelo D, Chambrin MC, Pomorski D, Ravaux P. ICU patient state characterization using machine learning in a time series framework. Proceedings of the AIMDM'99. Lecture Notes in Artificial Intelligence, vol.1620. Springer: Berlin, 1999; 356-360.
    • (1999) Proceedings of the AIMDM'99 , vol.1620 , pp. 356-360
    • Calvelo, D.1    Chambrin, M.C.2    Pomorski, D.3    Ravaux, P.4
  • 38
    • 0005717257 scopus 로고    scopus 로고
    • Time-oriented analysis of high frequency data in ICU monitoring
    • Lavrac N, Keravnou E, Zupan B (eds). Kluwer Academic Publishers: Dordrecht
    • Miksch S, Horn W, Popow C, Paky F. Time-oriented analysis of high frequency data in ICU monitoring. Intelligent Data Analysis in Medicine and Pharmacology,LavracN, Keravnou E, Zupan B (eds). Kluwer Academic Publishers: Dordrecht, 1997; 17-36.
    • (1997) Intelligent Data Analysis in Medicine and Pharmacology , pp. 17-36
    • Miksch, S.1    Horn, W.2    Popow, C.3    Paky, F.4
  • 39
    • 0030294410 scopus 로고    scopus 로고
    • Utilizing temporal data abstraction for data validation in therapy planning for artificially ventilated newborn infants
    • Miksch S, Horn W, Popow C, Paky F. Utilizing temporal data abstraction for data validation in therapy planning for artificially ventilated newborn infants. Artificial Intelligence in Medicine 1996; 8:543-576.
    • (1996) Artificial Intelligence in Medicine , vol.8 , pp. 543-576
    • Miksch, S.1    Horn, W.2    Popow, C.3    Paky, F.4
  • 40
    • 0030218093 scopus 로고    scopus 로고
    • The interpretation of time-varying data with DIAMON-1
    • Steimann F. The interpretation of time-varying data with DIAMON-1. Artificial Intelligence in Medicine 1996; 8: 343-357.
    • (1996) Artificial Intelligence in Medicine , vol.8 , pp. 343-357
    • Steimann, F.1
  • 41
    • 0032913269 scopus 로고    scopus 로고
    • Diagnostic monitoring in anaesthesia using fuzzy trend templates for matching temporal patterns
    • Lowe A, Harrison M, Jones R. Diagnostic monitoring in anaesthesia using fuzzy trend templates for matching temporal patterns. Artificial Intelligence in Medicine 1999; 16: 183-199.
    • (1999) Artificial Intelligence in Medicine , vol.16 , pp. 183-199
    • Lowe, A.1    Harrison, M.2    Jones, R.3
  • 42
    • 0035031278 scopus 로고    scopus 로고
    • The graphical presentation of decision support information in an intelligent anaesthesia monitor
    • Lowe A, Jones R, Harrison M. The graphical presentation of decision support information in an intelligent anaesthesia monitor. Artificial Intelligence in Medicine 2001; 22: 173-191.
    • (2001) Artificial Intelligence in Medicine , vol.22 , pp. 173-191
    • Lowe, A.1    Jones, R.2    Harrison, M.3
  • 43
    • 17644408377 scopus 로고    scopus 로고
    • On-line extraction of temporal episodes from ICU high-frequency data: A visual support for signal interpretation
    • Charbonnier S. On-line extraction of temporal episodes from ICU high-frequency data: a visual support for signal interpretation. Computer Methods and Programs in Biomedicine 2005; 78:115-132.
    • (2005) Computer Methods and Programs in Biomedicine , vol.78 , pp. 115-132
    • Charbonnier, S.1
  • 44
    • 0022101320 scopus 로고
    • A review of fuzzy set aggregation connectives
    • Dubois D, Prade H. A review of fuzzy set aggregation connectives. Information Sciences 1985; 36:85-121.
    • (1985) Information Sciences , vol.36 , pp. 85-121
    • Dubois, D.1    Prade, H.2
  • 45
    • 0027676898 scopus 로고
    • Families of OWA operators
    • Yager RR. Families of OWA operators. Fuzzy Sets and Systems 1993; 59:125-148.
    • (1993) Fuzzy Sets and Systems , vol.59 , pp. 125-148
    • Yager, R.R.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.