Development and Validation of a Deep Learning-based Automatic Auscultatory Blood Pressure Measurement Method

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.