• DocumentCode
    3176454
  • Title

    Atrial fibrillation detection algorithms for very long term ECG monitoring

  • Author

    Petrucci, E. ; Balian, V. ; Filippini, G. ; Mainardi, L.T.

  • Author_Institution
    Unita Coronarica, Busto Arsizio
  • fYear
    2005
  • fDate
    25-28 Sept. 2005
  • Firstpage
    623
  • Lastpage
    626
  • Abstract
    In this paper, we describe two algorithms suitable for the detection of Atrial Fibrillation episodes in very long terms (weeks) ECG monitoring, were the need of onboard implementation requires the development of reliable but simple and easy-to-implement methods. The proposed algorithms are based on the extraction of simple geometric features from the histogram of RR prematurity and delta RR. On the MIT Atrial fibrillation database, the RR prematurity algorithm provides the following performances: episodes sensitivity (S) 91%, episode positive Predictivity (P+) 92%, duration S 93%, duration P+ 97%. For the delta-RR algorithm the results were: episodes S 92%, episode P+ 78%, duration S 89%, duration P+ 90%
  • Keywords
    cardiovascular system; electrocardiography; feature extraction; medical signal processing; muscle; patient monitoring; signal detection; ECG monitoring detection; MIT atrial fibrillation database; RR prematurity algorithm; atrial fibrillation detection algorithms; feature extraction; Atrial fibrillation; Detection algorithms; Detectors; Electrocardiography; Histograms; Monitoring; Neural networks; Signal processing algorithms; Spatial databases; Testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computers in Cardiology, 2005
  • Conference_Location
    Lyon
  • Print_ISBN
    0-7803-9337-6
  • Type

    conf

  • DOI
    10.1109/CIC.2005.1588178
  • Filename
    1588178