| Title: | Identification of patients with atrial fibrillation: a big data exploratory analysis of the UK Biobank |
| Journal: | Physiological Measurement |
| Published: | 1 Feb 2020 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31978903/ |
| DOI: | https://doi.org/10.1088/1361-6579/ab6f9a |
| URL: | ~Ehttps://ora.ox.ac.uk/objects/uuid:d3b3924a-4f23-43fd-a455-236a2156ceb2/download_file?safe_filename=AF_detection_UKBiobank_rev1b%2B%28002%29.pdf&file_format=pdf&type_of_work=Journal+article~ |
Publication 5171
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Abstract
OBJECTIVE: Atrial fibrillation (AF) is the most common cardiac arrhythmia, with an estimated prevalence of around 1.6% in the adult population. The analysis of the electrocardiogram (ECG) data acquired in the UK Biobank represents an opportunity to screen for AF in a large sub-population in the UK. The main objective of this paper is to assess ten machine-learning methods for automated detection of subjects with AF in the UK Biobank dataset.APPROACH: Six classical machine-learning methods based on support vector machines are proposed and compared with state-of-the-art techniques (including a deep-learning algorithm), and finally a combination of a classical machine-learning and deep learning approaches. Evaluation is carried out on a subset of the UK Biobank dataset, manually annotated by human experts.
MAIN RESULTS: The combined classical machine-learning and deep learning method achieved an F1 score of 84.8% on the test subset, and a Cohen's kappa coefficient of 0.83, which is similar to the inter-observer agreement of two human experts.
SIGNIFICANCE: The level of performance indicates that the automated detection of AF in patients whose data have been stored in a large database, such as the UK Biobank, is possible. Such automated identification of AF patients would enable further investigations aimed at identifying the different phenotypes associated with AF.
7 Authors
- Julien Oster
- Jemma C Hopewell
- Klemen Ziberna
- Rohan Wijesurendra
- Christian F Camm
- Barbara Casadei
- Lionel Tarassenko
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