Key result
Noise-adapted AI outperforms standard models in detecting left ventricular systolic dysfunction on noisy single-lead ECGs.
Why the study?
Wearable devices could enable broad AI-based screening for left ventricular systolic dysfunction, but they frequently obtain noisy ECGs.
Does a noise-adapted deep learning model improve the detection of left ventricular systolic dysfunction from noisy single-lead ECGs compared to a standard model?
Observational (n=116,210)
No
Does a noise-adapted deep learning model improve the detection of left ventricular systolic dysfunction from noisy single-lead ECGs compared to a standard model?
Absolute Event Rate: 0.87% vs 0.72%
p-value: p=<0.001
A noise-adapted deep learning model trained on augmented single-lead ECGs maintains high accuracy for detecting left ventricular systolic dysfunction even in the presence of significant real-world noise, enabling reliable use in wearable devices.
May enable wearable LVSD screening in noisy settings; extends AI-ECG models but leaves prospective validation open.
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. 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.
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Khunte et al. (2022) conducted an observational in Left ventricular systolic dysfunction (n=116,210). Noise-adapted AI model vs. Standard AI model was evaluated on Detection of LVEF < 40% on ECGs augmented with portable ECG device noise at an SNR of 0.5 (AUROC) (95% CI 0.86-0.88, p=<0.001). A noise-adapted artificial intelligence model significantly improved the detection of left ventricular systolic dysfunction from noisy single-lead ECGs compared to a standard model (AUROC 0.87 vs 0.72).
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