Abstract
ABSTRACTArtificial 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. Overall, 385,601 ECGs were used for development of a standard and noise-adapted model. For the noise-adapted model, ECGs were augmented during training with random gaussian noise within four distinct frequency ranges, each emulating real-world noise sources. Both models performed comparably on clean ECGs with an AUROC of 0.90. The noise-adapted model performed 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 had 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.
Publisher
Cold Spring Harbor Laboratory