Heart failure (HF) is a complex clinical syndrome characterized by the inability of the heart to maintain adequate circulation to meet metabolic demands. Despite advances in therapy, HF remains associated with high morbidity, mortality, and healthcare costs, largely due to delayed diagnosis and limited accessibility of definitive imaging methods. Early identification, particularly in primary care settings, remains a major unmet clinical need [1].
To address this challenge, we revisited the concept of polycardiography using modern sensing technologies. Polycardiography enables synchronous acquisition of electrical and mechanical cardiovascular activity, providing access to electromechanical coupling parameters. We developed a prototype multimodal system capable of simultaneous recording of electrocardiogram (ECG), phonocardiogram (PCG), seismocardiogram (SCG), and photoplethysmogram (PPG) signals. [2] From these signals, established HF-related biomarkers can be extracted, including systolic time intervals (STIs) such as left ventricular ejection time (LVET) and pre-ejection period (PEP).
Prior to clinical deployment, the SensSmartTech validation study was conducted on healthy volunteers to verify device performance and characterize the dependence of key cardiovascular features on heart rate. This effort resulted in the SensSmartTech database, publicly available via PhysioNet [3,4], comprising multimodal cardiovascular recordings across a wide heart rate range. The database provides insight into physiological variability and supports algorithm development and benchmarking.
The central focus of this work is the SensSmart clinical study, conducted at the University Clinical Centre of Serbia. The study has been completed and comprehensive data analysis is underway. Its primary objective is to evaluate the non-inferiority of HF detection based on multimodal polycardiography relative to echocardiography. Preliminary results obtained using AI-based classification demonstrate promising performance in discriminating HF patients from controls, while also indicating the complementary diagnostic value of combining multiple sensing modalities. At the conference, we will present interim outcomes of the clinical analysis, including classification performance and an assessment of the relative contribution of individual multimodal features to HF detection.