Е-Публикации
Технически университет - София

Abstract: This paper presents a wireless, platform-independent system for synchronized acquisition of photoplethysmogram and single-channel electrocardiogram signals, designed primarily to support future research in cuff-less blood pressure estimation. The system integrates a dedicated sensor module that acquires the signals synchronously, ensuring precise temporal alignment. Signal acquisition and digital filtering are performed locally using an ESP32 system on a chip, which hosts an embedded WebSocket server for real-time data transmission. The client-side application, developed in React, provides a web-based interface for visualizing and recording signals, ensuring cross-platform compatibility.

References

1. T. Desquins, F. Bousefsaf, A. Pruski, and C. A Maaoui, “A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods,” Appl. Sci. 2022, 12, 9582, doi: https://doi.org/10.3390/app12199582, 2022.
2. K. B. Kim, and H. J. Baek, “Photoplethysmography in Wearable Devices: A Comprehensive Review of Technological Advances, Current Challenges, and Future Directions,” Electronics 2023, 12, 2923, doi: https://doi.org/10.3390/electronics12132923, 2023.
3. X. Huang, X. Zhang, R. Millham, L. Xu, and W. Wu, “Robust Estimation of Unsteady Beat-to-Beat Systolic Blood Pressure Trends Using Photoplethysmography Contextual Cycles,” Sensors 2025, 25, 3625, doi: https://doi.org/10.3390/s25123625, 2025.
4. W-H Lin, H. Wang, O.W. Samuel, and G. Li. “Using a new PPG indicator to increase the accuracy of PTT-based continuous cuffless blood pressure estimation,” Annu Int Conf IEEE Eng Med Biol Soc. 2017, pp. 738-741. doi: 10.1109/EMBC.2017.8036930, 2017.
5. S. Rajala, T. Ahmaniemi, H. Lindholm, and T. Taipalus, “Pulse arrival time (PAT) measurement based on arm ECG and finger PPG signals - comparison of PPG feature detection methods for PAT calculation,” Annu Int Conf IEEE Eng Med Biol Soc., pp. 250-253, doi: 10.1109/EMBC.2017.8036809, 2017.
6. N. Q. Mahardika T, Y. N. Fuadah, D. U. Jeong, and K. M. Lim, “PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNNLSTM,” Diagnostics (Basel). 2023 Aug 1, 13 (15):2566, doi: 10.3390/diagnostics13152566, 2023.
7. M. H. Chowdhury, M. N. I. Shuzan, M. E. H. Chowdhury, Z. B. Mahbub, M. M. Uddin, A. Khandakar, and M. B. I Reaz, “Estimating Blood Pressure from the Photoplethysmogram Signal and Demographic Features Using Machine Learning Techniques,“Sensors (Basel). 2020 Jun 1, 20 (11):3127, doi: 10.3390/s20113127, 2020.
8. H. Ji, and P. Zhou, “Advancing PPG-Based Continuous Blood Pressure Monitoring from a Generative Perspective,” Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems, Hangzhou, China, pp. 661-674, Association for Computing Machinery, New York, USA, doi: 10.1145/3666025.3699365, 2024.
9. J. Kyung, J-A Yang, J-H Choi, J-H Chang, S. Bae, J. Choi, and Y. Kim, “Deep-learning-based blood pressure estimation using multi channel photoplethysmogram and finger pressure with attention mechanism,” Scientific Reports, 13 (1):9311, doi: https://doi.org/10.1038/s41598-023-36068-6, 2023.
10. Q. (S) Xue, D. Nissanka, J. T. Yan, T. Tammy, R. Wang, S. Patel, and V. Iyer, “PPG Earring: Wireless Smart Earring for Heart Health Monitoring,” Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, USA, doi: 10.1145/3706598.3713856, 2025.
11. Y. Zhang, M. Berthelot, and B. Lo, “Wireless wearable photoplethysmography sensors for continuous blood pressure monitoring,” 2016 IEEE Wireless Health (WH), Bethesda, MD, USA, pp. 1-8, doi: 10.1109/WH.2016.7764560, 2016.
12. H. Li, T. Qu, L. Liu, Y. Li, Z. Hong and J. Xu, “An IoTEnabled Wearable Health Monitor for Synchronized ExG, PPG, and BioZ Measurement,” IEEE Transactions on Instrumentation and Measurement, vol. 74, pp. 1-11, doi: 10.1109/TIM.2025.3552863, 2025.
13. Espressif Systems, “ESP32 Technical Reference Manual,” Version 4.9, url: https://www.espressif.com/sites/default/files/documentation/esp32_technical_reference_manual_en.pdf, Last accessed: 2025-06-20.
14. Maxim Integrated, “Integrated Photoplethysmogram and Electrocardiogram Bio-Sensor Module For Mobile Health,” datasheet, 19-8402; Rev 2; 12/18, url: https://www.analog.com/media/en/technicaldocumentation/data-sheets/MAX86150.pdf, Last accessed: 2025-06-20.
15. Espressif Systems, “ESP-IDF Programming Guide,” url: https://docs.espressif.com/projects/esp-idf/en/latest/, Last accessed: 2025-07-29.
16. R. Barry, “Mastering the FreeRTOS Real Time Kernel,” Real Time Engineers Ltd.; eBook (Creative Commons Licensed), url: https://github.com/FreeRTOS/FreeRTOSKernel-Book/releases/download/V1.1.0/Mastering-theFreeRTOS-Real-Time-Kernel.v1.1.0.pdf, 2024, Last accessed: 2025-06-20.
17. National Institute of Standards and Technology, “Recommendation for key management: Part 1 – General (Rev. 5) (NIST Special Publication 800-57 Part 1 Rev. 5),” U.S. Department of Commerce, doi: https://doi.org/10.6028/NIST.SP.800-57pt1r5, 2020.
18. Recharts Group, “Redefined chart library built with React and D3,” url: https://github.com/recharts/recharts, Last accessed: 2025-06-20.
19. MUI, “Material UI: Comprehensive React component library that implements Google's Material Design. Free forever,” url: https://github.com/mui/material-ui, Last accessed: 2025-06-20.

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