A novel deep learning based automatic auscultatory method to measure blood pressure. (August 2019)

• Record Type: Journal Article
• Authors: Pan, Fan; He, Peiyu; Chen, Fei; Zhang, Jing; Wang, He; Zheng, Dingchang
• Is Part Of: International journal of medical informatics. Volume 128(2019)
• Page Start: 71
• Page End: 78
• Publication Date: 2019-08
• Subjects: BP blood pressure -- CNN convolutional neural network -- SBP systolicblood pressure -- DBP diastolicblood pressure -- KorS Korotkoff sounds -- STFT Shorttime Fourier transformation -- SD standard deviation -- ANOVA analysis of variance -- AAMI Association for the Advancement of Medical Instrumentation -- BHS British Hypertension Society -- RNN recurrent neural network -- EPSRC Engineering and Physical Sciences Research Council; Blood pressure measurement -- Convolutional neural network -- Manual auscultatory method -- Stethoscope position -- Stethoscope contact pressure; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2019.04.023
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: The new CNN-based method achieved high accurate BP measurement. Effect of stethoscope position on measured BPs confirmed by applying new method. Effect of skin contact pressure on measured BPs confirmed by applying new method. The new CNN-based method is an effective technique for non-invasive BP measurement. 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.