Department of Computational Intelligence, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Hypertension is the major root cause of blood pressure (BP) which in turn causes different cardiovascular diseases (CVDs). Hence BP need to be regularly monitored for preventing CVDs since it can be diagnosed and controlled through constant observation. Photoplethysmography (PPG) is identified as an important low-cost technology for facilitating a convenient and effective process in the early detection of CVDs. Different cardiovascular parameters such as blood oxygen saturation, heart rate, blood pressure, etc can be determined using the PPG technology. These cardiovascular parameters when given as input to the deep learning model is determined to diagnosis CVDs with maximized accuracy to an expected level. In this paper, Hybrid ResNet and Bidirectional LSTM-based Deep Learning Model (HRBLDLM) is proposed for diagnosing CVDs from PPG signals with due help in supporting the physicians during the process of continuous monitoring. This deep learning model mainly concentrated on the diagnosis of stage 1 hypertension, stage 2 hypertension, prehypertension, and normal CVDs with maximized accuracy using PPG signals. The PPG signals determined from PPG-BP dataset for investigation were recorded using IoT-based wearable patient monitoring (WPM) devices during the physical activity that includes high intensity, medium and low intensity movements involved driving, sitting and walking. The experiments conducted for this proposed deep learning model using PPG-BP dataset confirmed a better classification accuracy of 99.62% on par with the baseline PPG-based deep learning models contributed for detecting CVDs.
M. F. Ihsan, S. Mandala, and M. Pramudyo, “Study of Feature Extraction Algorithms on Photoplethysmography ( PPG ) Signals to Detect Coronary Heart Disease,” 2022 Int. Conf. Data Sci. Its Appl., pp. 300–304, 2022, doi: 10.1109/ICoDSA55874.2022.9862855.
S. Aziz, M. Awais, K. Iqtidar, and U. Qamar, “Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals,” no. April 2020, pp. 1–5, 2021, doi: 10.1111/exsy.12664.
S. E. Kjeldsen, “Hypertension and cardiovascular risk: General aspects,” Pharmacol. Res., vol. 129, pp. 95–99, 2018, doi: 10.1016/j.phrs.2017.11.003.
A. Alharbi, W. Alosaimi, R. Sahal, and H. Saleh, “Real-Time System Prediction for Heart Rate Using Deep Learning and Stream Processing Platforms,” vol. 2021, 2021.
F. Schrumpf, P. Frenzel, C. Aust, G. Osterhoff, and M. Fuchs, “Assessment of deep learning based blood pressure prediction from PPG and RPPG signals,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., pp. 3815–3825, 2021, doi: 10.1109/CVPRW53098.2021.00423.
M. Nitzan, A. Patron, Z. Glik, and A. T. Weiss, “Automatic noninvasive measurement of systolic blood pressure using photoplethysmography.,” Biomed. Eng. Online, vol. 8, p. 28, 2009, doi: 10.1186/1475-925X-8-28.
H. Elwahsh, E. El-shafeiy, S. Alanazi, and M. A. Tawfeek, “A new smart healthcare framework for real-time heart disease detection based on deep and machine learning,” PeerJ Comput. Sci., vol. 7, pp. 1–34, 2021, doi: 10.7717/PEERJ-CS.646.
S. González, W. T. Hsieh, and T. P. C. Chen, “A benchmark for machine-learning based non-invasive blood pressure estimation using photoplethysmogram,” Sci. data, vol. 10, no. 1, p. 149, 2023, doi: 10.1038/s41597-023-02020-6.
W. Ho, C. Liao, Y. J. Chen, K. Hwang, and Y. Tao, “Quickly Convert Photoplethysmography to Electrocardiogram Signals by a Banded Kernel Ensemble Learning Method for Heart Diseases Detection,” IEEE Access, vol. 10, pp. 51079–51092, 2022, doi: 10.1109/ACCESS.2022.3173176.
P. P. G. Bio-signals, J. Yu, S. Park, S. Kwon, K. Cho, and H. Lee, “AI-Based Stroke Disease Prediction System Using,” IEEE Access, vol. 10, pp. 43623–43638, 2022, doi: 10.1109/ACCESS.2022.3169284.
G. Georgieva-tsaneva and E. Gospodinova, “Cardiodiagnostics Based on Photoplethysmographic Signals,” 2022.
E. Susana, K. Ramli, P. D. Purnamasari, and N. H. Apriantoro, “Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis,” Information, vol. 14, no. 3, p. 145, 2023, doi: 10.3390/info14030145.
L. Dall’Olio et al., “Prediction of vascular aging based on smartphone acquired PPG signals,” Sci. Rep., vol. 10, no. 1, pp. 1–11, 2020, doi: 10.1038/s41598-020-76816-6.
P. Muntner et al., Measurement of blood pressure in humans: A scientific statement from the american heart association, vol. 73, no. 5. 2019. doi: 10.1161/HYP.0000000000000087.
J. Fine et al., Sources of inaccuracy in photoplethysmography for continuous cardiovascular monitoring, vol. 11, no. 4. 2021. doi: 10.3390/bios11040126.
B. Koteska, H. Mitrova, A. M. Bogdanova, and F. Lehocki, “Machine learning based SpO2 prediction from PPG signal’s characteristics features,” 2022 IEEE Int. Symp. Med. Meas. Appl. MeMeA 2022 - Conf. Proc., pp. 1–6, 2022, doi: 10.1109/MeMeA54994.2022.9856498.
T. Y. Abay and P. A. Kyriacou, “Photoplethysmography for blood volumes and oxygenation changes during intermittent vascular occlusions,” J. Clin. Monit. Comput., vol. 32, no. 3, pp. 447–455, 2018, doi: 10.1007/s10877-017-0030-2.
P. Ashish, K. Rama, and K. Manjeet, “A Review on Computation Methods Used in Photoplethysmography Signal Analysis for Heart Rate Estimation,” Arch. Comput. Methods Eng., vol. 29, no. 2, pp. 921–940, 2022, doi: 10.1007/s11831-021-09597-4.
E. Brophy, M. De Vos, G. Boylan, and T. Ward, “Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach,” pp. 1–12, 2021.
B. Huang, W. Chen, C. Lin, C. Juang, and J. Wang, “Biomedical Signal Processing and Control MLP-BP : A novel framework for cuffless blood pressure measurement with PPG and ECG signals based on MLP-Mixer neural networks,” Biomed. Signal Process. Control, vol. 73, no. December 2021, p. 103404, 2022, doi: 10.1016/j.bspc.2021.103404.
F. A. Putra, S. Mandala, and M. Pramudyo, “Study of Feature Selection Method to Detect Coronary Heart Disease ( CHD ) on Photoplethysmography ( PPG ) Signals,” vol. 4, no. 2, pp. 1018–1026, 2022, doi: 10.47065/bits.v4i2.2259.
C. Regressor, S. Ismail, I. Siddiqi, and U. Akram, “Heart rate estimation in PPG signals using,” Comput. Biol. Med., vol. 145, no. December 2021, p. 105470, 2022, doi: 10.1016/j.compbiomed.2022.105470.
A. S. Al Fahoum, A. O. Abu Al-Haija, and H. A. Alshraideh, “Identification of Coronary Artery Diseases Using Photoplethysmography Signals and Practical Feature Selection Process,” Bioengineering, vol. 10, no. 2, 2023, doi: 10.3390/bioengineering10020249.
S. R. Sinnapolu, GiriBabu and Alawneh, Shadi and Dixon, “A Method to Compute Electrical Activity of the Heart: Prediction and Analysis of Heart Diseases Using Novel Comma-Z Classifier and Gpu Framework (August 2, 2022). Available at SSRN: https://ssrn.,” https://ssrn.com/abstract=4179609, 2022.
C. J. Nichols et al., “Wearable Seismocardiography- Based Heart Disease,” vol. i, 2022, doi: 10.1161/JAHA.122.026067.
Acknowledgements
The authors would like to thank to the reviewers for nice comments on the manuscript.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Kalaiselvi Balaraman
Kalaiselvi Balaraman
Department of Computer Science, College of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chennai, India.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Kalaiselvi Balaraman and S.P.Angelin Claret, “Hybrid Resnet and Bidirectional LSTM-Based Deep Learning Model for Cardiovascular Disease Detection Using PPG Signals, Journal of Machine and Computing, vol.3, no.3, pp. 351-359, July 2023. doi: 10.53759/7669/jmc202303030.
