A comparative study of ProRithm and standard monitoring techniques for non-invasive blood pressure measurement using photoplethysmography and electrocardiography signals through artificial intelligence/machine learning methods

Authors

  • A. V. S. Suresh Department of Medical Oncology, Continental Hospitals, Hyderabad, Telangana, India
  • Vamsi Karatam Deepfacts, IIIT Hyderabad, Telangana, India
  • Dileep Karedla Deepfacts, IIIT Hyderabad, Telangana, India
  • Dinesh K. Babu Deepfacts, IIIT Hyderabad, Telangana, India
  • Pallavi Jha Deepfacts, IIIT Hyderabad, Telangana, India
  • Durga V. Bandireddy Deepfacts, IIIT Hyderabad, Telangana, India

DOI:

https://doi.org/10.18203/2394-6040.ijcmph20241611

Keywords:

Noninvasive blood pressure measurement, ProRithm device, Beat-to-beat cuffless monitoring, Multi-parameter monitoring devices, AI/ML methods, Remote monitoring

Abstract

Background: Multi-parameter monitoring devices are essential for providing real-time patient data, which is crucial for effective healthcare interventions. This clinical trial evaluated the accuracy of the ProRithm beat-to-beat cuffless device for arterial blood pressure monitoring, comparing it with a standard sphygmomanometer.

Methods: This observational study included 30 subjects aged 18 and above. Systolic and diastolic blood pressure measurements from both the ProRithm device and the Philips Monitor were compared using statistical analysis.

Results: The analysis revealed no statistically significant differences between the ProRithm device and the manual method. In comparison with manual measurements using a sphygmomanometer, the mean systolic blood pressure was 131.2 mmHg with ProRithm it was 129.3 mmHg. Similarly, with the manual method, while the mean diastolic blood pressure was 76.2 mmHg and with ProRithm it was 75.9 mmHg.

Conclusions: This study indicates that portable, small-sized devices like ProRithm, which facilitate remote monitoring, are effective for real-time blood pressure assessment in clinical settings.

References

Lu L, Zhang J, Xie Y, Gao F, Xu S, Wu X, et al. Wearable Health Devices in Health Care: Narrative Systematic Review. JMIR Mhealth Uhealth. 2020;8(11):e18907.

Stamler J, Dyer AR, Shekelle RB, Neaton J, Stamler R. Relationship of baseline major risk factors to coronary and all-cause mortality, and to longevity: findings from long-term follow-up of Chicago cohorts. Cardiology. 1993;82:191-222.

Cottrell E, McMillan K, Chambers R. A cross-sectional survey and service evaluation of simple telehealth in primary care: what do patients think? BMJ Open. 2012;2:e001392.

Samimi H, Dajani HR. A PPG-Based Calibration-Free Cuffless Blood Pressure Estimation Method Using Cardiovascular Dynamics. Sensors. 2023;23:4145.

Ismail SNA, Nayan NA, Jaafar R, May Z. Recent Advances in Non-Invasive Blood Pressure Monitoring and Prediction Using a Machine Learning Approach. Sensors (Basel). 2022;22(16):6195.

Marefat F, Erfani R, Mohseni P. A 1-V 8.1-µW PPG-recording front-end with >92-dB DR using light-to-digital conversion with signal-aware DC subtraction and ambient light removal. IEEE Solid-State. Circuits Lett. 2019;3:17-20.

Sagirova Z, Kuznetsova N, Gogiberidze N, Gognieva D, Suvorov A, Chomakhidze P, et al. Cuffless blood pressure measurement using a smartphone-case based ECG monitor with photoplethysmography in hypertensive patients. Sensors. 2021;21:3535.

Bote JM, Recas J, Hermida R. Evaluation of blood pressure estimation models based on pulse arrival time. Comput Electr Eng. 2020;84:106616.

Gan KB, Pua HL. Development of continuous blood pressure measurement system using photoplethysmograph and pulse transit time. Int J Robot Autom. 2021;3:8-12.

Singh O, Sunkaria RK. Detection of onset, systolic peak and dicrotic notch in arterial blood pressures pulses. Meas Control. 2017;50:170-6.

Baek S, Jang J, Yoon S. End-to-End blood pressure prediction via fully convolutional networks. IEEE Access. 2019;7:185458-68.

Ibtehaz N, Rahman MS. PPG2ABP: Translating photoplethysmogram (PPG) signals to arterial blood pressure (ABP) waveforms using fully convolutional neural networks. arXiv. 2020;2005.01669.

Baker S, Xiang W, Atkinson I. A hybrid neural network for continuous and non-invasive estimation of blood pressure from raw electrocardiogram and photoplethysmogram waveforms. Comput. Methods Programs Biomed. 2021;207:106191.

Downloads

Published

2024-06-10

How to Cite

Suresh, A. V. S., Karatam, V., Karedla, D., K. Babu, D., Jha, P., & Bandireddy, D. V. (2024). A comparative study of ProRithm and standard monitoring techniques for non-invasive blood pressure measurement using photoplethysmography and electrocardiography signals through artificial intelligence/machine learning methods. International Journal Of Community Medicine And Public Health. https://doi.org/10.18203/2394-6040.ijcmph20241611

Issue

Section

Original Research Articles