A novel deep learning based automatic auscultatory method to measure blood pressure

Abstract

Background: It is clinically important to develop innovative techniques that can accurately measure blood pressures (BP) automatically. Objectives: This study aimed to present and evaluate a novel automatic BP measurement method based on deep learning method, and to confirm the effects on measured BPs of the position and contact pressure of stethoscope. Methods: 30 healthy subjects were recruited. 9 BP measurements (from three different stethoscope contact pressures and three repeats) were performed on each subject. The convolutional neural network (CNN) was designed and trained to identify the Korotkoff sounds at a beat-by-beat level. Next, a mapping algorithm was developed to relate the identified Korotkoff beats to the corresponding cuff pressures for systolic and diastolic BP (SBP and DBP) determinations. Its performance was evaluated by investigating the effects of the position and contact pressure of stethoscope on measured BPs in comparison with reference manual auscultatory method. Results: The overall measurement errors of the proposed method were 1.4±2.4 mmHg for SBP and 3.3±2.9 mmHg for DBP from all the measurements. In addition, the method demonstrated that there were small SBP differences between the 2 stethoscope positions, respectively at the 3 stethoscope contact pressures, and that DBP from the stethoscope under the cuff was significantly lower than that from outside the cuff by 2.0 mmHg (P < 0.01). Conclusion: Our findings suggested that the deep learning based method was an effective technique to measure BP, and could be developed further to replace the current oscillometric based automatic blood pressure measurement method.

Deep learning-based automatic blood pressure measurement: evaluation of the effect of deep breathing, talking and arm movement

“…This study demonstrated that when compared with the reference manual auscultatory method, there was no significant difference between deep learning method and manual method under four different measurement conditions. Although we have previous reported that deep learning method could measure BPs accurately under resting condition with the measurement error of 1.4 ± 2.4 mmHg for SBP and 3.3 ± 2.9 mmHg for DBP, [9] it is important to evaluate its measurement performance under non-resting condition. In this study, the deep learning method achieved less than 1 mmHg measurement error (all SD < 4 mmHg) under both resting and non-resting condition (deeper breathing, talking and arm movement condition).…”

“…A deep learning method using convolutional neural networks (CNN) to identify the audible Korotkoff sound (KorS) has been developed in our previous publication [9]. As shown in Figure 1, after the audible KorS and non-audible KorS beats were identified by the trained CNN, the cuff pressures that corresponded to the first and last audible KorS beats were used to determine automatic SBP and DBP.…”

“…Chen et al proposed a deep neural network (DNN) based classifier to recognise audible heart sound with more than 91% accuracy [8]. We have recently developed a deep learning-based automatic auscultatory BP measurement method, where the convolutional neural network (CNN) was employed to identify the audible KorS sounds and a mapping algorithm was used to determine the automatic BP value [9]. In that paper, the accuracy of the deep learning-based BP measurement has been evaluated under resting condition with reference manual auscultatory method.…”

“…The aim of this study is to evaluate the performance of using deep learning-based method [9] to measure BPs and BP change under non-resting conditions (deeper breathing, talking, and arm movement) in comparison with the manual auscultatory method.…”

Covid-19 recognition from cough sounds using lightweight separable-quadratic convolutional network

“…For example, deep learning methods have been successfully utilized for post-stroke pneumonia prediction [2] . Deep CNNs have also been used for segmentation and classification of mammograms [3] and measurement of blood pressure [4] .…”