Online pattern recognition based on a generalized hidden Markov model for intraoperative vital sign monitoring

International Journal of Adaptive Control and Signal Processing

Volumn 24, Issue 5, 2010, Pages 363-381

  Yang, Ping ^a   Dumont, Guy A ^a   Ansermino, J Mark ^b  

^a 2332 Main Mall ^*  (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Change point detection; Generalized hidden Markov model; NIBPmean; Pattern recognition; Physiological monitoring; Switching Kalman filter

Indexed keywords

BAYESIAN INFERENCE; CHANGE POINT DETECTION; CHANGE-POINTS; CLINICAL DATA; COMPUTATIONAL OVERHEADS; CONTEXTUAL INFORMATION; FIRST-ORDER; GENERALIZED HIDDEN MARKOV MODEL; GENERALIZED HIDDEN MARKOV MODELS; GENERALIZED PSEUDO-BAYESIAN; HIDDEN SEMI-MARKOV MODELS; INTRA-OPERATIVE; KALMAN SMOOTHER; LINKED TABLES; MEMORY COMPLEXITY; NON-INVASIVE; PATTERN TRANSITIONS; PHYSIOLOGICAL MEASUREMENT; PHYSIOLOGICAL MONITORING; PRESSURE TRENDS; PROBABILITY OF OCCURRENCE; QUANTITATIVE EVALUATION; SECOND ORDERS; SIGNAL ESTIMATION; SIGNAL VALUE; SIMULATED SIGNALS; VITAL SIGN;

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; DATA FLOW ANALYSIS; DECISION SUPPORT SYSTEMS; DECISION THEORY; HIDDEN MARKOV MODELS; INFERENCE ENGINES; KALMAN FILTERS; LINEAR SYSTEMS; MEDICAL APPLICATIONS; PATIENT MONITORING; PATTERN RECOGNITION; PROBABILITY; SIGNAL DETECTION; SIGNAL PROCESSING; TIME SERIES;

PHYSIOLOGICAL MODELS;

EID: 77951530517     PISSN: 08906327     EISSN: 10991115     Source Type: Journal    
DOI: 10.1002/acs.1130     Document Type: Article

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