Pan, Fan;
He, Peiyu;
Wang, He;
Xu, Yuhang;
Pu, Xiaobo;
Zhao, Qijun;
Chen, Fei;
(2021)
Development and validation of a deep learning-based automatic auscultatory blood pressure measurement method.
Biomedical Signal Processing and Control
, 68
, Article 102742. 10.1016/j.bspc.2021.102742.
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Abstract
Manual auscultatory is the gold standard for clinical non-invasive blood pressure (BP) measurement, but its usage is decreasing as it requires substantial professional skills and training, and its environmental concerns related to mercury toxicity. As an alternative, automatic oscillometric technique has been used as one of the most common methods for BP measurement, however, it only estimates BPs based on empirical equations. To overcome these problems, this study aimed to develop a deep learning-based automatic auscultatory BP measurement method, and clinically validate its performance. A deep learning-based method that utilized time-frequency characteristics and temporal dependence of segmented Korotkoff sound (KorS) signals and employed convolutional neural network (CNN) and long short-term memory (LSTM) network was developed and trained using KorS and cuff pressure signals recorded from 314 subjects. The BPs determined by the manual auscultatory method was used as the reference for each measurement. The measurement error and BP category classification performance of our proposed method were then validated on a separate dataset of 114 subjects. Its performance in comparison with the oscillometric method was also comprehensively analyzed. The deep learning method achieved measurement errors of 0.2 ± 4.6 mmHg and 0.1 ± 3.2 mmHg for systolic BP and diastolic BP, respectively, and achieved high sensitivity, specificity and accuracy (all > 90 %) in classifying hypertensive subjects, which were better than those of the traditional oscillometric method. This validation study demonstrated that deep learning-based automatic auscultatory BP measurement can be developed to achieve high measurement accuracy and high BP category classification performance.
| Type: | Article |
|---|---|
| Title: | Development and validation of a deep learning-based automatic auscultatory blood pressure measurement method |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.bspc.2021.102742 |
| Publisher version: | https://doi.org/10.1016/j.bspc.2021.102742 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | ASSOCIATION, Blood pressure measurement, Deep learning, Engineering, Engineering, Biomedical, HYPERTENSION, Manual auscultatory method, MEASURING DEVICES, Oscillometric method, PART II, PREVENTION, RECOMMENDATIONS, Science & Technology, SOCIETY, Technology |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10215239 |
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