Wearable devices often pick up noisy electrocardiograms (ECGs), which can hinder artificial intelligence (AI)-based detection of cardiovascular disease.
In a paper titled, “Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices,” published in npj Digital Medicine, researchers from Yale Cardiovascular Medicine and Computer Science developed a noise-adapted AI model that can detect left ventricular systolic dysfunction (LVSD) from ECGs obtained by wearable devices.
Yale researchers used 385,601 ECGs for the development of a standard and noise-adapted AI model. To train the noise-adapted model, they augmented ECGs with custom noises in four frequency ranges, each emulating real-world noise sources. Both models performed similarly on standard electrocardiograms. But on tests with wearable device noise, the noise-adapted model detected LVSD significantly better.
Until now, AI diagnostic performance was poorer in real-world wearable ECG data compared to data adapted from 12-lead ECGs acquired in clinical settings. These findings may enable earlier detection of cardiomyopathies through scalable screening tools such as wearable devices.
Akshay Khunte et al, Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices, npj Digital Medicine (2023). DOI: 10.1038/s41746-023-00869-w
npj Digital Medicine
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