  Blood pressure monitoring is an essential component of hypertension
management and in the prediction of associated comorbidities. Blood pressure is
a dynamic vital sign with frequent changes throughout a given day. Capturing
blood pressure remotely and frequently (also known as ambulatory blood pressure
monitoring) has traditionally been achieved by measuring blood pressure at
discrete intervals using an inflatable cuff. However, there is growing interest
in developing a cuffless ambulatory blood pressure monitoring system to measure
blood pressure continuously. One such approach is by utilizing bioimpedance
sensors to build regression models. A practical problem with this approach is
that the amount of data required to confidently train such a regression model
can be prohibitive. In this paper, we propose the application of the
domain-adversarial training neural network (DANN) method on our multitask
learning (MTL) blood pressure estimation model, allowing for knowledge transfer
between subjects. Our proposed model obtains average root mean square error
(RMSE) of $4.80 \pm 0.74$ mmHg for diastolic blood pressure and $7.34 \pm 1.88$
mmHg for systolic blood pressure when using three minutes of training data,
$4.64 \pm 0.60$ mmHg and $7.10 \pm 1.79$ respectively when using four minutes
of training data, and $4.48 \pm 0.57$ mmHg and $6.79 \pm 1.70$ respectively
when using five minutes of training data. DANN improves training with minimal
data in comparison to both directly training and to training with a pretrained
model from another subject, decreasing RMSE by $0.19$ to $0.26$ mmHg
(diastolic) and by $0.46$ to $0.67$ mmHg (systolic) in comparison to the best
baseline models. We observe that four minutes of training data is the minimum
requirement for our framework to exceed ISO standards within this cohort of
patients.
