Key result
A domain-adversarial neural network model using four minutes of training data achieved an average root mean square error of 4.64 mmHg for diastolic and 7.10 mmHg for systolic blood pressure, meeting ISO standards.
Why the study?
Developing cuffless ambulatory blood pressure monitoring using bioimpedance sensors is hindered by the prohibitive amount of data required to train regression models.
Does a domain-adversarial neural network (DANN) improve the accuracy of cuffless blood pressure estimation from wearable bioimpedance sensors using minimal training data compared to direct training or pretrained models?
Does a domain-adversarial neural network (DANN) improve the accuracy of cuffless blood pressure estimation from wearable bioimpedance sensors using minimal training data compared to direct training or pretrained models?
A domain-adversarial neural network approach enables accurate, personalized cuffless blood pressure estimation from wearable bioimpedance sensors using only four minutes of calibration data.
Cuffless ambulatory BP monitoring remains investigational; leaves open rigorous validation against cuff standards before clinical adoption.
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 ± 0.74 mmHg for diastolic blood pressure and 7.34 ± 1.88 mmHg for systolic blood pressure when using three minutes of training data, 4.64 ± 0.60 mmHg and 7.10 ± 1.79 respectively when using four minutes of training data, and 4.48±0.57 mmHg and 6.79±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.
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Zhang et al. (2020) studied Blood pressure estimation (n=11). Domain-adversarial neural network (DANN) model vs. Directly trained model and pretrained model was evaluated on Root mean square error (RMSE) for diastolic and systolic blood pressure. A domain-adversarial neural network model using four minutes of training data achieved an average root mean square error of 4.64 mmHg for diastolic and 7.10 mmHg for systolic blood pressure, meeting ISO standards.
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