Synchronous post-exercise electrocardiogram, phonocardiogram, photoplethysmograms and seismocardiogram

• Published in: Scientific data Vol. 12; no. 1; pp. 1452 - 12
• Main Authors: Lazović, Aleksandar, Atanasoski, Vladimir, Tadić, Predrag, Djordjević, Natalija, Tiosavljević, Maša, Ivanović, Marija D., Hadžievski, Ljupčo, Ristić, Arsen, Vukčević, Vladan, Petrović, Jovana
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.08.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/443/592/75/230; 639/166/985; Biomarkers; Blood pressure; Cardiovascular system; Congestive heart failure; Data Descriptor; Digitization; Disease; EKG; Electrocardiography; Electrodes; Exercise; Exercise Test; Heart; Heart diseases; Heart Rate; Humanities and Social Sciences; Humans; Integrated circuits; multidisciplinary; Phonocardiography; Photoplethysmography; Science; Science (multidisciplinary); Sensors; California; United States > US; Texas; Massachusetts
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-025-05781-4

Noninvasive electromechanical assessment of cardiovascular function is emerging as a cost-effective method for diagnosis of heart failure and arterial diseases, and for telemedical monitoring of blood pressure and neural disorders. It encompasses simultaneous acquisition of electrocardiographic, phonocardiographic, arterial-pulse, chest-vibration, bioimpedance and other waveforms. The phases and amplitudes of these waveforms are used for construction of disease biomarkers. The procedure includes corrections of biomarker values to daily variation and excursions of heart rate. However, datasets that enable a systematic study of the effects of heart rate on mechanical waveforms are currently not available. Here, we describe SensSmartTech - the first dataset of multiparametric cardiovascular signals systematically measured in a large span of heart rates from 52 to 182 beats per minute, achieved by running on a treadmill. Besides providing the data for biomarker correction, the dataset enables new insights into the cardio-respiratory and electro-mechanical couplings in the cardiovascular system.