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(2), the model yielded a mean bias of 0.07 L/min/m(2) with LoA from -0.35 to 0.48 L/min/m(2). This study demonstrates the feasibility of using deep learning in combination with wearable SCG sensors to non-invasively estimate CO. Model performance was particularly strong in low-output states. These findings highlight the potential of SCG-based monitoring to augment clinical decision-making in settings where invasive measurements are impractical or unavailable. Prospective multicenter validation is needed to confirm generalizability and assess clinical impact.