Article Synopsis
- Artificial intelligence can effectively identify left ventricular systolic dysfunction (LVSD) using electrocardiograms (ECGs), even with the challenges posed by noisy signals from wearable devices.* -
- A new approach was developed that enhances AI models to better detect cardiovascular diseases by training them with augmented noisy ECG data that mimics real-world conditions.* -
- The noise-adapted AI model outperformed the standard model on ECGs affected by device noise, achieving better accuracy (AUROC of 0.87 vs. 0.72), showcasing its potential for improving remote cardiovascular monitoring.*
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Article Abstract
Artificial intelligence (AI) can detect left ventricular systolic dysfunction (LVSD) from electrocardiograms (ECGs). Wearable devices could allow for broad AI-based screening but frequently obtain noisy ECGs. We report a novel strategy that automates the detection of hidden cardiovascular diseases, such as LVSD, adapted for noisy single-lead ECGs obtained on wearable and portable devices. We use 385,601 ECGs for development of a standard and noise-adapted model. For the noise-adapted model, ECGs are augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models perform comparably on standard ECGs with an AUROC of 0.90. The noise-adapted model performs significantly better on the same test set augmented with four distinct real-world noise recordings at multiple signal-to-noise ratios (SNRs), including noise isolated from a portable device ECG. The standard and noise-adapted models have an AUROC of 0.72 and 0.87, respectively, when evaluated on ECGs augmented with portable ECG device noise at an SNR of 0.5. This approach represents a novel strategy for the development of wearable-adapted tools from clinical ECG repositories.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10336107 | PMC |
| http://dx.doi.org/10.1038/s41746-023-00869-w | DOI Listing |
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