GenECG: a synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development

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Objectives

An image-based ECG dataset incorporating visual imperfections common to paper-based ECGs, which are typically scanned or photographed into electronic health records, could facilitate clinically useful artificial intelligence (AI)-ECG algorithm development. This study aimed to create a high-fidelity, synthetic image-based ECG dataset.

Methods

ECG images were recreated from the PTB-XL database, a signal-based dataset and image manipulation techniques were applied to mimic imperfections associated with ECGs in real-world settings. Clinical Turing tests were conducted to evaluate the fidelity of the synthetic images, and the performance of current AI-ECG algorithms was assessed using synthetic images containing visual imperfections.

Results

GenECG, an image-based dataset containing 21 799 ECGs with visual imperfections encountered in routine clinical care paired with imperfection-free images, was created. Turing tests confirmed the realism of the images: expert observer accuracy of discrimination between real-world and synthetic ECGs fell from 63.9% (95% CI 58.0% to 69.8%) to 53.3% (95% CI 48.6% to 58.1%) over three rounds of testing, indicating that observers could not distinguish between synthetic and real ECGs. The performance of pre-existing algorithms on synthetic (area under the curve (AUC) 0.592, 95% CI 0.421 to 0.763) and real-world (AUC 0.647, 95% CI 0.520 to 0.774) ECG images containing imperfections was limited. Algorithm fine-tuning with GenECG data improved real-world ECG classification accuracy (AUC 0.821, 95% CI 0.730 to 0.913) demonstrating its potential to augment image-based algorithm development.

Discussion/conclusion

GenECG is the first synthetic image-based ECG dataset to pass a clinical Turing test. The dataset will enable image-based AI-ECG algorithm development, ensuring utility in low resource areas, prehospital settings and hospital environments where signal data are unavailable.

Bodagh, N., Tun, K. S., Barton, A., Javidi, M., Rashid, D., Burns, R., Kotadia, I., Klis, M., Gharaviri, A., Vigneswaran, V., Niederer, S., ONeill, M., Bernabeu, M. O., Williams, S. E.

Bodagh, N., Tun, K. S., Barton, A., Javidi, M., Rashid, D., Burns, R., Kotadia, I., Klis, M., Gharaviri, A., Vigneswaran, V., Niederer, S., ONeill, M., Bernabeu, M. O., Williams, S. E.

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