The 12-lead electrocardiogram (ECG) is a fundamental instrument for diagnosing cardiac abnormalities. Traditionally, 12-lead ECG are analyzed by trained medical professionals, however recent advances in Artificial Intelligence (AI) and, in particular, deep neural networks [1] have enabled methods to accurately analyze ECGs [2, 3, 4].
Such methods proved capable of recognizing specific patterns and abnormalities in ECG waveforms invisible to the human eye, e.g. detecting cardiac contractile dysfunction from an “apparently” normal ECG [5] or the presence of an underlying atrial fibrillation from a sinus rhythm ECG [6]. Altogether, these findings highlight the potential of an AI-based ECG analysis, with significant implications for early detection and management of different cardiac abnormalities.
This work aims at assessing whether an AI is capable of identifying in a single lead cardiac abnormalities that are typically diagnosed from standard 12-lead ECGs. The potential outcomes of this point are significant: if an AI would be able to detect cardiac abnormalities in single-lead ECGs, that would be a strong incentive towards integrating diagnostic AIs into wearable devices. The perspective of single-lead ECGs diagnoses on wearable devices would be game-changer, as it would allow for frequent, accessible and economic screening for large masses of population for both cardiovascular and non-cardiovascular diseases.
You need to have docker installed on your machine, for more info see this document: https://docs.docker.com/engine/installation/.
Ensure your user has the rights to run docker (without the use of sudo). To create the docker group and add your user:
Create the docker group.
$ sudo groupadd docker
Add your user to the docker group.
$ sudo usermod -aG docker $USER
Log out and log back in so that your group membership is re-evaluated.
- Go to the kaggle website.
- Click on Your profile button on the top right and then select Account.
- Scroll down to the API section and click on the Create New Token button.
- It will initiate the download of a file call kaggle.json. Save the file at a known location on your machine.
- Then move the kaggle.json to ~/.kaggle location, if ~/.kaggle does’t exist you can create a directory in home with the same name.
To reproduce the results presented in the paper run:
./reproduce.sh
We tested the docker on the following GPUs: NVIDIA GeForce 1080, NVIDIA GeForce 1080ti, NVIDIA P106-100
[1] LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324.
[2] Siontis, K. C., Noseworthy, P. A., Attia, Z. I., and Friedman, P. A. (2021). Artificial intelligence-enhanced electrocardiography in cardiovascular disease management. Nature Reviews Cardiology 18, 465–478
[3] Hong, S., Zhou, Y., Shang, J., Xiao, C., and Sun, J. (2020). Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Computers in Biology and Medicine 122, 103801. doi:https://doi.org/10.1016/j.compbiomed.2020.103801
[4] Huang, Y.-C., Hsu, Y.-C., Liu, Z.-Y., Lin, C.-H., Tsai, R., Chen, J.-S., et al. (2023). Artificial intelligence-enabled electrocardiographic screening for left ventricular systolic dysfunction and mortality risk prediction. Frontiers in Cardiovascular Medicine 10. doi:10.3389/fcvm.2023.1070641
[5] Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., et al. (2019a). Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nature medicine 25, 70–74
[6] Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., et al. (2019b). An artificial intelligence-enabled ecg algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet 394, 861–867
Copyright Daniele Baccega, Andrea Saglietto, Attilio Fiandrotti, Roberto Esposito
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