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
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