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Automatic Screening of Atrial Fibrillation in Thumb-ECG Recordings

Stridh, Martin LU and Rosenqvist, Marten (2012) 39th Conference on Computing in Cardiology In 2012 Computing in Cardiology (Cinc), Vol 39 p.193-196
Abstract
The present study proposes a novel sorting algorithm for identification of patients with atrial fibrillation in large one-lead ECG repositories. Repeated measurements at home with automatic transmission of data to a central database is presently tested in the search for atrial fibrillation for the long-term purpose to reduce the incidence of stroke. Such screening rapidly generates large databases of signals waiting to be sorted and prioritized. The one-lead ECGs were first preprocessed to remove baseline wander followed by beat detection and beat classification. A rhythm analysis stage was employed to perform RR interval analysis with negligible influence of ectopic beats and disturbances. RR interval information in combination with a... (More)
The present study proposes a novel sorting algorithm for identification of patients with atrial fibrillation in large one-lead ECG repositories. Repeated measurements at home with automatic transmission of data to a central database is presently tested in the search for atrial fibrillation for the long-term purpose to reduce the incidence of stroke. Such screening rapidly generates large databases of signals waiting to be sorted and prioritized. The one-lead ECGs were first preprocessed to remove baseline wander followed by beat detection and beat classification. A rhythm analysis stage was employed to perform RR interval analysis with negligible influence of ectopic beats and disturbances. RR interval information in combination with a waveform clustering procedure applied to the expected P wave intervals were used to sort the database into a low priority group containing mainly sinus rhythm, a high priority group containing all ECGs with irregular beat patterns, and a third group showing an unreliable RR series. The outcome of the algorithm was compared to an annotated database containing 2837 one-lead ECG recordings from 103 patients where each recording was visually inspected by a physician. The proposed method was able to divide the database into a low-priority group containing 93% (n=2357) of the sinus rhythm cases and a high priority group containing 98% (n=55) of the atrial fibrillation cases. In addition, 3.7% were found to have an unreliable RR series. In conclusion, automatic analysis of one-lead ECG databases can quickly guide the physician to find recordings with high probability to contain atrial fibrillation and can automatically indicate if a recording needs to be remade due to quality problems. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
2012 Computing in Cardiology (Cinc), Vol 39
pages
193 - 196
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
39th Conference on Computing in Cardiology
external identifiers
  • wos:000320270700049
  • scopus:84875638108
ISSN
0276-6574
language
English
LU publication?
yes
id
f5709206-0ca7-4ccd-911c-5295522fa29d (old id 3979978)
date added to LUP
2013-09-03 12:55:25
date last changed
2017-11-05 04:02:54
@inproceedings{f5709206-0ca7-4ccd-911c-5295522fa29d,
  abstract     = {The present study proposes a novel sorting algorithm for identification of patients with atrial fibrillation in large one-lead ECG repositories. Repeated measurements at home with automatic transmission of data to a central database is presently tested in the search for atrial fibrillation for the long-term purpose to reduce the incidence of stroke. Such screening rapidly generates large databases of signals waiting to be sorted and prioritized. The one-lead ECGs were first preprocessed to remove baseline wander followed by beat detection and beat classification. A rhythm analysis stage was employed to perform RR interval analysis with negligible influence of ectopic beats and disturbances. RR interval information in combination with a waveform clustering procedure applied to the expected P wave intervals were used to sort the database into a low priority group containing mainly sinus rhythm, a high priority group containing all ECGs with irregular beat patterns, and a third group showing an unreliable RR series. The outcome of the algorithm was compared to an annotated database containing 2837 one-lead ECG recordings from 103 patients where each recording was visually inspected by a physician. The proposed method was able to divide the database into a low-priority group containing 93% (n=2357) of the sinus rhythm cases and a high priority group containing 98% (n=55) of the atrial fibrillation cases. In addition, 3.7% were found to have an unreliable RR series. In conclusion, automatic analysis of one-lead ECG databases can quickly guide the physician to find recordings with high probability to contain atrial fibrillation and can automatically indicate if a recording needs to be remade due to quality problems.},
  author       = {Stridh, Martin and Rosenqvist, Marten},
  booktitle    = {2012 Computing in Cardiology (Cinc), Vol 39},
  issn         = {0276-6574},
  language     = {eng},
  pages        = {193--196},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Automatic Screening of Atrial Fibrillation in Thumb-ECG Recordings},
  year         = {2012},
}