Δ BP-Net: Monitoring "Changes" in Blood Pressure Using PPG with Self-contrastive Masking.

Estimating blood pressure (BP) values from physiological signals (e.g., photoplethysmogram (PPG)) using deep learning models has recently received increased attention, yet challenges remain in terms of models' generalizability. Here, we propose taking a new approach by framing the problem as tracking the "changes" in BP over an interval, rather than directly estimating its value. Indeed, continuous monitoring of acute changes in BP holds promising implications for clinical applications (e.g., hypertensive emergencies). As a solution, we first present a self-contrastive masking (SCM) model, designed to perform pair-wise temporal comparisons within the input signal. We then leverage the proposed SCM model to introduce ΔBPNet, a model trained to detect elevations/drops greater than a given threshold in the systolic blood pressure (SBP) over an interval, from PPG. Using data from PulseDB, 1) we evaluate the performance of ΔBP-Net on previously unseen subjects, 2) we test ΔBP-Net's ability to generalize across domains by training and testing on different datasets, and 3) we compare the performance of ΔBP-Net with existing PPG-based BP-estimation models in detecting over-threshold SBP changes. Formulating the problem as a binary classification task (i.e., over-threshold SBP elevation/ drop or not), ΔBP-Net achieves 75.97%/73.19% accuracy on data from subjects unseen during training. Additionally, the proposed ΔBP-Net outperforms ΔSBP estimations derived from existing PPG-based BP-estimation methods. Overall, by shifting the focus from estimating the value of SBP to detecting overthreshold "changes" in SBP, this work introduces a new potential for using PPG in clinical BP monitoring, and takes a step forward in addressing the challenges related to the generalizability of PPG-based BP-estimation models.

First-In-Human Demonstration of High Frequency Electrical Motor Nerve Block: Case Report.

ABSTRACT: This feasibility study tested the capability of high frequency stimulation (HFS) to block muscle contractions elicited by electrical stimulation of the same nerve proximally. During a tendon lengthening surgery in the forearm, the anterior interosseous nerve (AIN) was exposed. A specialized nerve cuff electrode was placed around the nerve, and a stimulating probe held on the nerve 1 cm proximal to the cuff electrode delivered pulses of current causing the pronator quadratus muscle to contract. Through the cuff electrode, 20 kHz HFS was delivered to the nerve for 10 seconds during proximal stimulation. HFS amplitudes between 5 and 10 mA peak-to-peak were tested to determine which produced complete and partial block of the electrically induced contractions. The minimum HFS amplitude that produced complete block was 8 mA, with lower amplitudes producing partial block. In all trials, muscle contractions resumed immediately after HFS was turned off. This demonstration of high frequency electrical nerve block is a milestone in the road to clinical implementation of HFS mediated motor block for spasticity.