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CLC number: R541.75

On-line Access: 2012-09-04

Received: 2012-04-05

Revision Accepted: 2012-07-02

Crosschecked: 2012-07-12

Cited: 11

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE B 2012 Vol.13 No.9 P.751-756


High accuracy in automatic detection of atrial fibrillation for Holter monitoring

Author(s):  Kai Jiang, Chao Huang, Shu-ming Ye, Hang Chen

Affiliation(s):  Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang University, Hangzhou 310058, China

Corresponding email(s):   ch-sun@263.net

Key Words:  Atrial fibrillation, Delta RR interval distribution difference curve, Holter monitoring

Kai Jiang, Chao Huang, Shu-ming Ye, Hang Chen. High accuracy in automatic detection of atrial fibrillation for Holter monitoring[J]. Journal of Zhejiang University Science B, 2012, 13(9): 751-756.

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author="Kai Jiang, Chao Huang, Shu-ming Ye, Hang Chen",
journal="Journal of Zhejiang University Science B",
publisher="Zhejiang University Press & Springer",

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%T High accuracy in automatic detection of atrial fibrillation for Holter monitoring
%A Kai Jiang
%A Chao Huang
%A Shu-ming Ye
%A Hang Chen
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.B1200107

T1 - High accuracy in automatic detection of atrial fibrillation for Holter monitoring
A1 - Kai Jiang
A1 - Chao Huang
A1 - Shu-ming Ye
A1 - Hang Chen
J0 - Journal of Zhejiang University Science B
VL - 13
IS - 9
SP - 751
EP - 756
%@ 1673-1581
Y1 - 2012
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.B1200107

atrial fibrillation (AF) has been considered as a growing epidemiological problem in the world, with a substantial impact on morbidity and mortality. Ambulatory electrocardiography (e.g., Holter) monitoring is commonly used for AF diagnosis and therapy and the automated detection of AF is of great significance due to the vast amount of information provided. This study presents a combined method to achieve high accuracy in AF detection. Firstly, we detected the suspected transitions between AF and sinus rhythm using the delta RR interval distribution difference curve, which were then classified by a combination analysis of P wave and RR interval. The MIT-BIH AF database was used for algorithm validation and a high sensitivity and a high specificity (98.2% and 97.5%, respectively) were achieved. Further, we developed a dataset of 24-h paroxysmal AF Holter recordings (n=45) to evaluate the performance in clinical practice, which yielded satisfactory accuracy (sensitivity=96.3%, specificity=96.8%).

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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