Comprehensive Dataset and Signal Processing Framework for Phonocardiogram-Based Heart Rate and Blood Pressure Estimation

Abstract

Cardiovascular diseases (CVDs) represent significant global health challenges today, necessitating regular and reliable monitoring to enable early intervention. Phonocardiogram (PCG) signals present a promising non-invasive method for assessing cardiovascular health. While recent studies have focused on estimating heart rate (HR) from PCG signals and blood pressure (BP) through multimodal combinations with other physiological data, reliable and cost-effective systems that can predict both HR and BP using only PCG signals remain largely unexplored. In this study, we proposed and developed a lab-scale cost-effective Phonocardiogram Tracking (PhonoTrack) system that can measure both HR and BP using only the PCG signal. We also introduced a corresponding dataset collected from 15 participants to evaluate the effectiveness of the proposed system. HR was determined using several peak detection methods, such as Hilbert Transform (HT), Shannon Entropy (SE), and WES, achieving notable Pearson correlation coefficients of 0.965, 0.973, and 0.955, respectively. The corresponding root mean square errors (RMSEs) were 2.467 bpm, 1.688 bpm, and 1.992 bpm for HT, SE, and WES, respectively. Additionally, we developed an advanced semi-empirical model based on multiple regression techniques to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). This model demonstrated standard deviations of 2.10 mmHg for SBP and 3.20 mmHg for DBP across all subjects, with Pearson correlation coefficients of 0.89 and 0.70, respectively. These findings pave the way for developing a non-invasive, low-cost, and portable PhonoTrack device, positioning it as a promising solution for continuous cardiovascular monitoring settings.