Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices

Autor: Akshay Khunte, Veer Sangha, Evangelos K. Oikonomou, Lovedeep S. Dhingra, Arya Aminorroaya, Bobak J. Mortazavi, Andreas Coppi, Cynthia A. Brandt, Harlan M. Krumholz, Rohan Khera
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: npj Digital Medicine, Vol 6, Iss 1, Pp 1-10 (2023)
Druh dokumentu: article
ISSN: 2398-6352
DOI: 10.1038/s41746-023-00869-w
Popis: 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.
Databáze: Directory of Open Access Journals
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