Automatic Screening of Atrial Fibrillation in Thumb-ECG Recordings
(2012) 39th Conference on Computing in Cardiology 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3979978
- author
- Stridh, Martin LU and Rosenqvist, Marten
- organization
- publishing date
- 2012
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 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
- conference location
- Krakow, Poland
- conference dates
- 2012-09-09 - 2012-09-12
- 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
- 2016-04-01 13:51:02
- date last changed
- 2022-01-27 21:27:15
@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}},
}