MEMS Fingertip Strain Plethysmography for Cuffless Estimation of Blood Pressure

To develop a cuffless method for estimating blood pressure (BP) from fingertip strain plethysmography (SPG) recordings.

A custom-built micro-electromechanical systems (MEMS) strain sensor is employed to record heartbeat-induced vibrations at the fingertip. An XGboost regressor is then trained to relate SPG recordings to beat-to-beat systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP) values. For this purpose, each SPG segment in this setup is represented by a feature vector consisting of cardiac time interval, amplitude features, statistical properties, and demographic information of the subjects. In addition, a novel concept, coined geometric features, are introduced and incorporated into the feature space to further encode the dynamics in SPG recordings. The performance of the regressor is assessed on 32 healthy subjects through 5-fold cross-validation (5-CV) and leave-subject-out cross validation (LSOCV).

Mean absolute errors (MAEs) of 3.88 mmHg and 5.45 mmHg were achieved for DBP and SBP estimations, respectively, in the 5-CV setting. LSOCV yielded MAEs of 8.16 mmHg for DBP and 16.81 mmHg for SBP. Through feature importance analysis, 3 geometric and 26 integral-related features introduced in this work were identified as primary contributors to BP estimation. The method exhibited robustness against variations in blood pressure level (normal to critical) and body mass index (underweight to obese), with MAE ranges of [1.28, 4.28] mmHg and [2.64, 7.52] mmHg, respectively.

The findings suggest high potential for SPG-based BP estimation at the fingertip.

This study presents a fundamental step towards the augmentation of optical sensors that are susceptible to dark skin tones.