Lightweight end-to-end model-based korotkoff sounds phase identification and blood pressure measurement_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

JIANG Zhiyu ¹ , KOU Wenyi ¹ , LI Li ² , ZHAO Qijun ³ ,  QIAN Yongjun ⁴ ,  PAN Fan ¹

1. School of Electronic Information, Sichuan University, Chengdu, 610065, P. R. China;
2. Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 610041, P. R. China;
3. School of Computer Science, Sichuan University, Chengdu, 610065, P. R. China;
4. Department of Cardiovascular Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

QIAN Yongjun, Email: qianyongjun@scu.edu.cn; PAN Fan, Email: panfan@scu.edu.cn

Keywords：

Korotkoff sounds phase identification; blood pressure measurement; end-to-end model; deep learning; artificial intelligence

DOI：

10.7507/1007-4848.202507061

Video：

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Objective To propose a lightweight end-to-end neural network model for automated Korotkoff sound phase recognition and subsequent blood pressure (BP) measurement, aiming to improve measurement accuracy and population adaptability. Methods We developed a streamlined architecture integrating depthwise separable convolution (DSConv), multi-head attention (MHA), and bidirectional gated recurrent unit (BiGRU). The model directly processes Korotkoff sound time-series signals to identify auscultatory phases. Systolic BP (SBP) and diastolic BP (DBP) were determined using Phase Ⅰ and PhaseⅤdetections, respectively. Given the clinical relevance of phase Ⅳ for specific populations (e.g., children and pregnant women, denoted as DBPIV), BP values from this phase were also recorded. Results The study enrolled 106 volunteers with 70 males, 36 females at mean age of (40.0±12.0) years. The model achieved 94.25% phase recognition accuracy. Measurement errors were (0.1±2.5) mm Hg (SBP), (0.9±3.4) mm Hg (DBPIV), and (0.8±2.6) mm Hg (DBP). Conclusion Our method enables precise phase recognition and BP measurement, demonstrating potential for developing population-adaptive blood pressure monitoring systems.

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23.	Pan F, Chen F, Liu C, et al. Quantitative comparison of Korotkoff sound waveform characteristics: effects of static cuff pressures and stethoscope positions. Ann Biomed Eng, 2018, 46(11): 1736-1744.
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1. Babbs CF. The origin of Korotkoff sounds and the accuracy of auscultatory blood pressure measurements. J Am Soc Hypertens, 2015, 9(12): 935-950.
2. Geddes LA, Hoff HE, Badger AS. Introduction of the auscultatory method of measuring blood pressure—including a translation of Korotkoff's original paper. Cardiovasc Res Cent Bull, 1966, 5(2): 57-74.
3. Beevers G, Lip GYH, O'brien E. ABC of hypertension - blood pressure measurement part Ⅱ— conventional sphygmomanometry: technique of auscultatory blood pressure measurement. BMJ, 2001, 322(7293): 1043-1047.
4. 王文, 张维忠, 孙宁玲, 等. 中国血压测量指南, 2011, 19(12): 1101-1115.Wang W, Zhang WZ, Sun NL, et al. China blood pressure measurement guidelines. Chin J Hypertens, 2011, 19(12): 1101-1115.
5. Beardmore Gray A, Dyer R, Shennan A. Blood pressure measurement in pregnancy. Int J Obstet Anesth, 2019, 37: 137-138.
6. Brown MA, Reiter L, Smith B, et al. Measuring blood pressure in pregnant women: a comparison of direct and indirect methods. Am J Obstet Gynecol, 1994, 171(3): 661-667.
7. Higgins JR, de Swiet M. Blood-pressure measurement and classification in pregnancy. Lancet, 2001, 357(9250): 131-135.
8. Meng Z, Zhu X, Chen F, et al. Effects of room environment and nursing experience on clinical blood pressure measurement: an observational study. Blood Press Monit, 2017, 22(2): 79-85.
9. Agnihotram KC, Kumar PW, Muntner P, et al. International consensus on standardized clinic blood pressure measurement - a call to action. Am J Med, 2023, 136(5): 438-445. e1.
10. 段以卓, 邹玉宝. 临床上无创血压测量的方法及应用. 中国分子心脏病学杂志, 2024, 24(1): 5928-5932.Duan YZ, Zou YB. Methods and application of clinical non-invasive blood pressure measurement. Mol Cardiol Chin, 2024, 24(1): 5928-5932.
11. 胡欣宇, 王昕波, 赵召龙, 等. 基于柯氏音法与示波法结合的新型血压测量系统. 软件, 2017, 38(3): 78-81.Hu XY, Wang XB, Zhao ZL, et al. A new blood pressure measurement system based on oscillographic and Korotkoff-sound method. Comput Eng Softw, 2017, 38(3): 78-81.
12. Zhang MY, Cui X, Zhao LW, et al. A lightweight deep learning based bowel sounds segmentation algorithm for gastrointestinal (GI) monitoring. Eng Appl Artif Intell, 2024, 127: 101684.
13. Xiao D, Zhu F, Jiang J, et al. Leveraging natural cognitive systems in conjunction with ResNet50-BiGRU model and attention mechanism for enhanced medical image analysis and sports injury prediction. Front Neurosci, 2023, 17: 1292858.
14. Fatima T, Yang WB, Xia KW, et al. Deep learning approach combining GAN and BiGRU for diabetes prediction. In: Proceedings of the 3rd International Conference on Robotics, Artificial Intelligence and Intelligent Control (RAIIC); 2024 Jul 5-7; Mianyang, China. New York: IEEE; 2024.
15. Hossain S, Chakrabarty A, Gadekallu TR, et al. Vision transformers, ensemble model, and transfer learning leveraging explainable AI for brain tumor detection and classification. IEEE J Biomed Health Inform, 2024, 28(3): 1261-1272.
16. Ding Y, Zhang S, Tang CG, et al. MASA-TCN: multi-anchor space-aware temporal convolutional neural networks for continuous and discrete EEG emotion recognition. IEEE J Biomed Health Inform, 2024, 28(7): 3953-3964.
17. Fan P, He P, He W, et al. Development and validation of a deep learning-based automatic auscultatory blood pressure measurement method. Biomed Signal Process Control, 2021, 68: 102713.
18. Pan F, He PY, Chen F, et al. A novel deep learning based automatic auscultatory method to measure blood pressure. Int J Med Inform, 2019, 128: 71-78.
19. Argha A, Celler BG, Lovell NH. A novel automated blood pressure estimation algorithm using sequences of Korotkoff sounds. IEEE J Biomed Health Inform, 2020, 24(10): 2744-2754.
20. Argha A, Celler BG, Alinejad-Rokny H, et al. Blood pressure estimation from Korotkoff sound signals using an end-to-end deep-learning-based algorithm. IEEE Trans Instrum Meas, 2022, 71: 4002610.
21. 陈俊辉, 何培宇, 方安成, 等. 基于深度学习的柯氏音时相分类研究. 中国胸心血管外科临床杂志, 2023, 30(1): 25-31.Chen JH, He PY, Fang AC, et al. Research on classification of Korotkoff sound phases based on deep learning. Chin J Clin Thorac Cardiovasc Surg, 2023, 30(1): 25-31.
22. Pickering TG, Hall JE, Appel LJ, et al. Recommendations for blood pressure measurement in humans and experimental animals: part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation, 2005, 111(5): 697-716.
23. Pan F, Chen F, Liu C, et al. Quantitative comparison of Korotkoff sound waveform characteristics: effects of static cuff pressures and stethoscope positions. Ann Biomed Eng, 2018, 46(11): 1736-1744.
24. Chollet F. Xception: deep learning with depthwise separable convolutions. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017 Jul 21-26; Honolulu, HI. New York: IEEE; 2017. p. 1800-1807.
25. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017 Dec 4-9; Long Beach, CA. Red Hook: Curran Associates Inc. 2017: 5998-6008.
26. Chung J, Gulcehre C, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. Preprint. Posted online December 11, 2014. arXiv: 1412.3555.

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