ArticleThe American journal of cardiology2026
Deep Learning Predicts Cardiac Output from Seismocardiographic Signals in Heart Failure.
Article in The American journal of cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Clinical Readiness of Hemodynamic Estimation from Cardiac Vibration Signals: An Endpoint-Specific Critical Review.Annals of biomedical engineering · 2026Review
- Convergent Sensing: Integrating Biometric and Environmental Monitoring in Next-Generation Wearables.Biosensors · 2026Review
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4 authors.
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Abstract
Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks, and reliance on specialized infrastructure limit accessibility and scalability of standard-of-care right heart catheterization (RHC). Seismocardiography (SCG), a non-invasive technique which records subtle chest wall vibrations generated by cardiac mechanical activity, may offer a promising alternative for CO determination. To explore this potential, we developed and evaluated a deep learning model for estimating CO directly from SCG, electrocardiogram (ECG), and body mass index (BMI) in heart failure patients undergoing RHC. We trained a deep convolutional neural network for CO estimation using an open-access dataset comprising 73 heart failure patients with simultaneous RHC, SCG, and ECG recordings. Model performance was evaluated on 64 patients using pairwise nested leave-pair-out cross-validation. When estimating CO in patients with a reference output < 6 L/min, the deep learning model achieved a mean bias of -0.01 L/min with LoA from -0.88 to 0.87 L/min. When predicting cardiac index in patients with a reference index < 2.2 L/min/m
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