a-STEP: Oscillometry-Augmented Auscultation Method for Improved Blood Pressure Measurement

Accurate blood pressure measurement is crucial for effective health monitoring, with oscillometric and auscultation methods being commonly employed. However, oscillometric measurements are population-dependent, and manual auscultation relies heavily on operator skill. This study introduces the a-STEP (Augmented Short Time Energy Peaks) algorithm, designed to combine both methods while automating the process, making it user-friendly, and aiming to provide accurate blood pressure measurements. The proposed algorithm was rigorously evaluated on a dataset comprising 350 recordings with manually annotated systolic blood pressure (SBP) and diastolic blood pressure (DBP) for ground truth value. Implementation of the algorithm yielded successful identification of both SBP and DBP, with errors of -1.5 ± 6.6 mmHg and 3.8 ± 7.6 mmHg, respectively, with in-significant bias (p <0.0001). The agreement between the a-STEP algorithm results and the reference measurement was strong and statistically significant, exhibiting correlation coefficients (r) of 0.94 (p <0.0001) for SBP and 0.79 (p <0.0001) for DBP. Bland-Altman plots further supported strong agreement, with confidence intervals of -14.5 mmHg to 11.4 mmHg for SBP and - 11.1 mmHg to 18.6 mmHg for DBP. According to the performance assessment, the algorithm received an ‘A’ grade in the BHS grading system and successfully met the accuracy requirements prescribed by AAMIIANSI and ISO. The proposed algorithm potentially led to the development of automated devices capable of providing accurate blood pressure measurements without relying on population or individual-level calibration coefficients. The feasibility of integrating software and hardware components was examined in this context by an in-vivo study conducted on 13 participants.