Assisted Prediction of Hypertension Based on Heart Rate Variability and Improved Residual Networks

Authors: Yong Zhao, Jian He, Cheng Zhang

Abstract:

Cardiovascular disease resulting from hypertension poses a significant threat to human health, and early detection of hypertension can potentially save numerous lives. Traditional methods for detecting hypertension require specialized equipment and are often incapable of capturing continuous blood pressure fluctuations. To address this issue, this study starts by analyzing the principle of heart rate variability (HRV) and introduces the utilization of sliding window and power spectral density (PSD) techniques to analyze both temporal and frequency domain features of HRV. Subsequently, a hypertension prediction network that relies on HRV is proposed, combining Resnet, attention mechanisms, and a multi-layer perceptron. The network leverages a modified ResNet18 to extract frequency domain features, while employing an attention mechanism to integrate temporal domain features, thus enabling auxiliary hypertension prediction through the multi-layer perceptron. The proposed network is trained and tested using the publicly available SHAREE dataset from PhysioNet. The results demonstrate that the network achieves a high prediction accuracy of 92.06% for hypertension, surpassing traditional models such as K Near Neighbor (KNN), Bayes, Logistic regression, and traditional Convolutional Neural Network (CNN).

Keywords: Feature extraction, heart rate variability, hypertension, residual networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.10906092

References:

[1] C. Troeger, M. Forouzanfar, P. C. Rao, I. Khalil, A. Brown, R. C. Reiner, N. Fullman, R. L. Thompson, A. Abajobir, M. Ahmed et al., “Estimates of global, regional, and national morbidity, mortality, and aetiologies of diarrhoeal diseases: a systematic analysis for the global burden of disease study 2015,” The Lancet infectious diseases, vol. 17, no. 9, pp. 909–948, 2017.
[2] A. Meidert, J. Briegel, and B. Saugel, “Principles and pitfalls of arterial blood pressure measurement,” Der Anaesthesist, vol. 68, pp. 637–650, 2019.
[3] P. Lamba and K. Rawal, “A survey of algorithms for feature extraction and feature classification methods,” in 2019 International Conference on Automation, Computational and Technology Management (ICACTM). IEEE, 2019, pp. 338–341.
[4] C. M. van Ravenswaaij-Arts, L. A. Kollee, J. C. Hopman, G. B. Stoelinga, and H. P. van Geijn, “Heart rate variability,” Annals of internal medicine, vol. 118, no. 6, pp. 436–447, 1993.
[5] J. P. ZBILUT and L. LAWSON, “Decreased heart rate variability in significant cardiac events,” Critical care medicine, vol. 16, no. 1, pp. 64–66, 1988.
[6] G. Wilson, “A review of “the polyvagal theory: Neurophysiological foundations of emotions, attachment, communication, and self regulation” stephen w. porges.(2011). new york: Ww norton, 272 pp., $45.00 (hardback).” 2012.
[7] B. Folkow, “Physiological aspects of primary hypertension.” Physiological reviews, vol. 62, no. 2, pp. 347–504, 1982.
[8] S. Oparil, “The sympathetic nervous system in clinical and experimental hypertension,” Kidney international, vol. 30, no. 3, pp. 437–452, 1986.
[9] B. Xhyheri, O. Manfrini, M. Mazzolini, C. Pizzi, and R. Bugiardini, “Heart rate variability today,” Progress in cardiovascular diseases, vol. 55, no. 3, pp. 321–331, 2012.
[10] Z. Abrishami and H. Tabatabaee, “Design of a fuzzy expert system and a multi-layer neural network system for diagnosis of hypertension,” Bull Environ Pharmacol Life Sci, vol. 4, no. 11, pp. 138–145, 2015.
[11] Y. Ren, H. Fei, X. Liang, D. Ji, and M. Cheng, “A hybrid neural network model for predicting kidney disease in hypertension patients based on electronic health records,” BMC medical informatics and decision making, vol. 19, pp. 131–138, 2019.
[12] A. Wang, N. An, G. Chen, L. Li, and G. Alterovitz, “Predicting hypertension without measurement: A non-invasive, questionnaire-based approach,” Expert Systems with Applications, vol. 42, no. 21, pp. 7601–7609, 2015.
[13] P. Pavithran, M. Madanmohan, R. Mithun, M. Jomal, and H. Nandeesha, “Heart rate variability in middle-aged men with new-onset hypertension,” Annals of Noninvasive Electrocardiology, vol. 13, no. 3, pp. 242–248, 2008.
[14] A. Gunther, I. Salzmann, S. Nowack, M. Schwab, R. Surber, H. Hoyer, O. Witte, and D. Hoyer, “Heart rate variability–a potential early marker of sub-acute post-stroke infections,” Acta neurologica Scandinavica, vol. 126, no. 3, pp. 189–196, 2012.
[15] K.-c. Lan, P. Raknim,W.-F. Kao, and J.-H. Huang, “Toward hypertension prediction based on ppg-derived hrv signals: A feasibility study,” Journal of medical systems, vol. 42, pp. 1–7, 2018.
[16] K. M. Warren, J. R. Harvey, K. H. Chon, and Y. Mendelson, “Improving pulse rate measurements during random motion using a wearable multichannel reflectance photoplethysmograph,” Sensors, vol. 16, no. 3, p. 342, 2016.
[17] PhysioNet, https://www.physionet.org/content/shareedb.
[18] H. Kawano, R. Okada, and K. Yano, “Histological study on the distribution of autonomic nerves in the human heart,” Heart and vessels, vol. 18, no. 1, p. 32, 2003.
[19] J. L. Hamilton and L. B. Alloy, “Atypical reactivity of heart rate variability to stress and depression across development: Systematic review of the literature and directions for future research,” Clinical psychology review, vol. 50, pp. 67–79, 2016.
[20] C. Schiweck, D. Piette, D. Berckmans, S. Claes, and E. Vrieze, “Heart rate and high frequency heart rate variability during stress as biomarker for clinical depression. a systematic review,” Psychological medicine, vol. 49, no. 2, pp. 200–211, 2019.
[21] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
[22] A. M. Alsayat, “Efficient genetic k-means clustering algorithm and its application to data mining on different domains,” Ph.D. dissertation, Bowie State University, 2016.
[23] D. Niedermayer, “An introduction to bayesian networks and their contemporary applications,” Innovations in Bayesian networks: Theory and applications, pp. 117–130, 2008.
[24] C.-c. Chen, K. Mondal, P. Vervliet, A. Covaci, E. P. O’Brien, K. J. Rockne, J. L. Drummond, and L. Hanley, “Logistic regression analysis of lc-ms/ms data of monomers eluted from aged dental composites: A supervised machine-learning approach,” Analytical Chemistry, 2023.
[25] A. Roslin, M. Lebedev, T. Mitchell, I. Onederra, and C. Leonardi, “Processing of micro-ct images of granodiorite rock samples using convolutional neural networks (cnn). part iii: Enhancement of scanco micro-ct images of granodiorite rocks using a 3d convolutional neural network super-resolution algorithm,” Minerals Engineering, vol. 195, p. 108028, 2023.