Finding unusual medical time-series subsequences: Algorithms and applications

IEEE Transactions on Information Technology in Biomedicine

Volumn 10, Issue 3, 2006, Pages 429-439

  Keogh, Eamonn ^a   Lin, Jessica ^a,b   Fu, Ada Waichee ^c   Van Herle, Helga ^d  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b GEORGE MASON UNIVERSITY   (United States)

^c CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^d UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Anomaly detection; Clustering; Time series data mining

Indexed keywords

ALGORITHMS; DATA MINING; ELECTROCARDIOGRAPHY; TIME SERIES ANALYSIS;

ANOMALY DETECTION; MEDICAL DATASETS; TIME-SERIES DATA MINING;

BIOMEDICAL ENGINEERING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; CLUSTER ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; ELECTROCARDIOGRAPHY; HUMAN; INFORMATION RETRIEVAL; METHODOLOGY; POLYSOMNOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; TIME;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROCARDIOGRAPHY; HUMANS; INFORMATION STORAGE AND RETRIEVAL; MODELS, BIOLOGICAL; PATTERN RECOGNITION, AUTOMATED; POLYSOMNOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TIME FACTORS;

EID: 33746921193     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2005.863870     Document Type: Article

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