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
Link To Document :
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