Level Accuracy of Automatic and Real Time Detection of Atrial Fibrillation with A New Wireless ECG Recorder (The SmartCardia)
R Braojos, S Murali, F Rincon and JJ Goy*
August 18, 2023
DOI : 10.56831/PSEN-03-072
Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia but is currently under-diagnosed since it can be asymptomatic. Early detection of AF could be highly beneficial for the prevention of stroke, which is a major risk associated with AF, with a fivefold increase. The advent of portable monitoring devices can help uncover the underlying dynamics of human health in a way that has not been possible before.
Method: The purpose of this study was to validate the automated analysis of AF by the SmartCardia’s proprietary health monitoring device (ScaAI patch, SmartCardia S.A., Lausanne, Switzerland). To this end, a model was created and tested on three publicly available databases comprised of 243,960 ECG segments of 30-seconds. The model was further tested against a set of 500 ECG streams of 30-seconds (recorded by ScaAI patch - across different clinical trials; annotated by 3 different cardiologists), especially representing problematic conditions, when determination the underlying rhythm was challenging.
Results: The created model obtained F1-scores of 94.42 against a test set from two published available databases, and an F1-score of 92.61 (average F1-scores w.r.t each cardiologist) on the SmartCardia assembled database.
Conclusion: We demonstrated that the new wireless ScalAl patch had a high capacity to automatically detect AF when compared with public database. Further studies will help identify the optimal role of the the ScaAl patch in the management of cardiac arrhythmias.
Keywords: Atrial fibrillation; arrhythmias; automatic arrhythmia detection; wireless system
- S Stewart., et al. “Population prevalence, incidence, and predictors of atrial fibrillation in the Renfrew/Paisley study”. Heart 8 (2001): 516-521.
- Blum S., et al. “Incidence and Predictors of Atrial Fibrillation Progression”. Journal of the American Heart Association 8 (2019): e012554.
- William M., et al. “Prevalence, Age Distribution, and Gender of Patients with Atrial FibrillationAnalysis and Implications”. Arch Intern Med 155.5 (1995): 469-473.
- Vermond RA., et al. “Incidence of Atrial Fibrillation and Relationship with Cardiovascular Events, Heart Failure, and Mortality: A Community-Based Study from the Netherlands”. J Am Coll Cardiol 66.9 (2015): 1000-7.
- Benjamin Emelia J., et al. “Impact of Atrial Fibrillation on the Risk of Death”. Circulation 98 (1998): 946-952.
- Schläpfer J and Wellens HJ. “Computer-interpreted electrocardiograms: benefits and limitations”. J. Am. Coll. Cardiol 70 (2017): 1183-1192.
- Shah AP and Rubin SA. “Errors in the computerized electrocardiogram interpretation of cardiac rhythm”. J. Electrocardiol 40 (2007): 385-390.
- Guglin ME and Thatai D. “Common errors in computer electrocardiogram interpretation”. Int. J. Cardiol 106 (2006): 232-237.
- Poon K, Okin PM and Kligfield P. “Diagnostic performance of a computer-based ECG rhythm algorithm”. J. Electrocardiol 38 (2005): 235-238.
- Gulshan V., et al. “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”. JAMA 316 (2016): 2402-2410.
- Esteva A. “Dermatologist-level classification of skin cancer with deep neural networks”. Nature 542 (2017): 115-118.
- Poungponsri S and Yu X. “An adaptive filtering approach for electrocardiogram (ECG) signal noise reduction using neural networks”. Neurocomputing 117 (2013): 206-213.
- Ochoa A, Mena LJ and Felix VG. “Noise-tolerant neural network approach for electrocardiogram signal classification”. In Proc. 3rd International Conference on Compute and Data Analysis (2017): 277-282.
- Javadi M., et al. “Classification of ECG arrhythmia by a modular neural network based on mixture of experts and negatively correlated learning”. Biomed. Signal Process. Control 8 (2013): 289-296.
- Acharya UR., et al. “A deep convolutional neural network model to classify heartbeats”. Comput. Biol. Med 89 (2017): 389-396.
- Banupriya CV and Karpagavalli S. “Electrocardiogram beat classification using probabilistic neural network”. In Proc. fea Learning: Challenges and Opportunities Ahead (2014): 31-37.
- Al Rahhal MM., et al. “Deep learning approach for active classification of electrocardiogram signals”. Inf. Sci. (NY) 345 (2016): 340-354.
- Acharya UR., et al. “Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network”. Inf. Sci. (NY) 405 (2017): 81-90.
- Zihlmann M, Perekrestenko D and Tschannen M. “Convolutional recurrent neural networks for electrocardiogram classification”. Comput. Cardiol (2017).
- Xiong Z, Zhao J and Stiles MK. “Robust ECG signal classification for detection of atrial fibrillation using a novel neural network”. Comput. Cardiol (2017).
- Clifford G., et al. “AF classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017”. Comput. Cardiol (2017).
- Teijeiro T., et al. “Arrhythmia classification from the abductive interpretation of short single-lead ECG records”. Comput. Cardiol (2017).
- Tison GH., et al. “Passive detection of atrial fibrillation using commercially available smatwatch”. JAMA Cardiology 3 (2018): 409-416.
- Moody GB and Mark RG. “A new method for detecting atrial fibrillation using R-R intervals”. Computers in Cardiology 10 (1983): 227-230.
- Mark RG., et al. “An annotated ECG database for evaluating arrhythmia detectors”. IEEE Transactions on Biomedical Engineering 29.8 (1982): 600.
- Moody GB and Mark RG. “The MIT-BIH Arrhythmia Database on CD-ROM and software for use with it”. Computers in Cardiology 17 (1990): 185-188.
- Goldberger AL., et al. “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals”. Circulation 101.23 (2000): e215-e220.
- Hand DJ and Till RJ. “A simple generalisation of the area under the ROC curve for multiple class classification problems”. Mach. Learn 45 (2001): 171-186.
- Fawcett T. “An introduction to ROC analysis”. Pattern Recognit. Lett 27 (2006): 861-874.
- Sanders P., et al. “Reveal LINQ Usability Investigators. Performance of a new atrial fibrillation detection algorithm in a miniaturized insertable cardiac monitor: Results from the Reveal LINQ Usability Study”. Heart rhythm 13 (2016): 1425-1430.