UTransBPNet: A General Deep Learning Model for Cuffless Blood Pressure Estimation under Activities

Abstract

<p>Cuffless blood pressure (BP) monitoring has gained great attention in the past twenty years considering its significant benefits in cardiovascular healthcare. However, the main challenge of this technology is the inaccurate BP modeling under activities, i.e., existing work have either been inappropriately validated with sufficient intra-individual BP variations, or did not show promising estimation accuracy under activities. In this study, a novel deep learning model <em>UTransBPNet</em>, featured in short- and long-range feature representation, is proposed aiming to improve the estimation accuracy and tracking capability of intra-individual BP changes under activities. The model performance was comprehensively evaluated in three different datasets, i.e., one public dataset (Dataset_MIMIC) and two datasets under daily activities (Dataset_Drink and Dataset_Exercise). Under subject-independent validation, the model achieved state-of-the-art performance in the Dataset_MIMIC, with the mean absolute differences (MADs) for systolic BP (SBP) and diastolic BP (DBP) of 4.38 and 2.25 mmHg, respectively. In addition, the model achieved strong tracking capability of intra-individual BP variations under activities, with the individual Pearson’ correlation coefficients for SBP and DBP of 0.61±0.17 and 0.62±0.13 (Dataset_Drink), 0.82±0.11 and 0.72±0.18 (Dataset_Exercise), respectively. Moreover, this study for the first time tested the generalization capability across different activities, and showed that with small-sized scenario-specific data for finetuning, our model showed good cross-scenario generalization capability which however degraded significantly when there are differences in the BP distribution and variation patterns between the datasets. In conclusion, <em>UTransBPNet</em> is generally a very promising deep learning model for accurate cuffless BP estimation and tracking BP changes. Future work should further investigate the influence of BP distribution and variation patterns on the generalization capability for building effective training dataset. </p>

Validation of deep learning models for cuffless blood pressure estimation on a large benchmarking dataset

“…Therefore, we have reported the accuracy of ABP to cater to this critical requirement. However, we also recognize that other clinical settings may prioritize discrete measurements of SBP, DBP, and MBP over continuous waveforms, and many previous studies have only reported the prediction performance of models for SBP and DBP [8,16] . To facilitate a broader comparison with existing research, we have included performance metrics for both SBP and DBP predictions in this study.…”

“…Another study proposed a lightweight deep learning model, KD-informer, and accurately predicted continuous BP on two separate datasets, but the intra-subject BP variations were not reported [8] . Additionally, a recent study assessed several machine learning models such as ridge regression, SVM, AdaBoost, and random forest, as well as deep learning methods including VGGNet16, ResNet50, BiLSTM, and ResLSTM, on a large-scale dataset gathered from 3,077 individuals without severe cardiovascular diseases using smartwatches [16] . They reported that the best-performing calibration-free model had estimation errors of -0.71 ± 13.04 and -0.29 ± 8.78 mmHg for SBP and DBP, respectively.…”