Scalable customization of atrial fibrillation detection in cardiac monitoring devices: Increasing detection accuracy through personalized monitoring in large patient populations

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2011, Pages 2184-2187

  Jang, Kuk Jin ^a   Balakrishnan, Guha ^b   Syed, Zeeshan ^b   Verma, Naveen ^a  

^a Princeton University   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; ATRIAL FIBRILLATION; CARDIAC MONITORING; DETECTION ACCURACY; LABELED DATA; PATIENT POPULATION; PATIENT SPECIFICITY; PATIENT STATE; PERFORMANCE APPROACH; REMOTE MONITORING; SCALABLE METHODS;

DISEASES; SCALABILITY;

DETECTORS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; ELECTROCARDIOGRAPHY; HEART ATRIUM FIBRILLATION; HUMAN; METHODOLOGY; PERSONALIZED MEDICINE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ATRIAL FIBRILLATION; DIAGNOSIS, COMPUTER-ASSISTED; ELECTROCARDIOGRAPHY; HUMANS; INDIVIDUALIZED MEDICINE; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84862264364     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2011.6090411     Document Type: Conference Paper

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