Estimation-Theoretic Bias Reduction for Oscillometric Blood Pressure Readings

TL;DR

This paper develops an estimation-theoretic approach to correct systematic biases in oscillometric blood pressure measurements, improving accuracy by modeling measurement errors and respiration effects.

Contribution

It introduces LS and ML estimation frameworks that incorporate prior knowledge to reduce biases in cuff-based BP readings, enhancing measurement reliability.

Findings

ML method outperforms LS in bias correction

Incorporating priors improves measurement accuracy

Respiration effects significantly impact BP measurement precision

Abstract

Oscillometry is the standard method for non-invasive, cuff-based blood pressure (BP) measurement, but it introduces systematic errors that may impact clinical accuracy. This study investigates the sources of these errors--primarily the limitations of oscillometry itself and respiration-induced fluctuations--using BP waveform data from the MIMIC database. Oscillometry tends to underestimate systolic BP and overestimate diastolic BP, while respiration introduces cyclical variations that further degrade measurement precision. To mitigate these effects, we propose an estimation-theoretic framework employing least squares (LS) and maximum likelihood (ML) methods for correcting both single and repeated BP measurements. LS estimation supports conventional multi-measurement averaging protocols, whereas the ML approach incorporates prior knowledge of measurement errors, offering improved performance. Our results demonstrate that leveraging statistical priors across multiple readings can enhance the accuracy of non-invasive BP monitoring, with potential implications for improving cardiovascular diagnosis and treatment.