ArticleNPJ digital medicine2023
Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- Detection of Severe Structural Heart Disease Using an AI-Enhanced Portable 1-Lead ECG: The ACCESS-SHD Study.medRxiv : the preprint server for health sciences · 2026Article
- R-R Interval Histogram-Based Deep Learning for 3-Class Atrial Fibrillation Screening in Garment-Type Wearable Holter Electrocardiogram Monitoring: Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Bridging the gap from clinical to home ECG: quantifying and overcoming accuracy loss in AI-enabled single-lead ECG models.NPJ digital medicine · 2026Article
- An Edge-Cloud Collaborative ECG-Assisted Diagnostic System Leveraging Cross-Lead Knowledge Distillation and Large Language Models.Sensors (Basel, Switzerland) · 2026Article
- An in-sensor communication electronic textile for imperceptible and ultrarobust silent speech.Nature communications · 2026Article
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation.Current heart failure reports · 2026Review
- Optimizing single-lead ECG axis for AI-based detection of myocardial diseases.NPJ cardiovascular health · 2026Article
- Wearable devices and cardiovascular health: revolutionizing remote monitoring and disease prevention.European heart journal · 2026Review
- Emerging Artificial Intelligence Tools for the Screening of Structural and Valvular Heart Disease.Current heart failure reports · 2026Review
- Wearable-Echo-FM: an ECG echo foundation model for 1-lead electrocardiography.European heart journal. Digital health · 2026Article
- Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification.Current heart failure reports · 2026Review
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- Robust AI-ECG for Predicting Left Ventricular Systolic Dysfunction in Pediatric Congenital Heart Disease.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- Artificial intelligence-enabled analysis of handheld single-lead electrocardiograms to predict incident atrial fibrillation: an analysis of the VITAL-AF randomized trial.NPJ digital medicine · 2025Article
- Clinical care of family members of patients with dilated cardiomyopathy.European heart journal · 2025Article
- Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems.JACC. Advances · 2025Review
- Barriers to translating continuous monitoring technologies for preventative medicine.Nature biomedical engineering · 2025Review
- An integrated algorithm for single lead electrocardiogram signal analysis using deep learning with 12-lead data.Scientific reports · 2025Article
- Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.European heart journal · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.