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
A. V. S. Suresh, Vamsi Karatam, Dileep Karedla, Dinesh K.Babu, Pallavi Jha, Durga V. Bandireddy
Department of Medical Oncology, Continental Hospitals, Hyderabad, Telangana, India
Deepfacts, IIIT Hyderabad, Telangana, India
Received: 28May2024
Revised: 01June2024
Accepted: 04June2024
Correspondence: Dr.A. V. S. Suresh
attilivss@gmail.com
Copyright: © the author(s), publisher and licensee Medip Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 comparedusing 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.
Keywords: Non-invasive blood pressure measurement, proRITHM device, Beat-to-beat cuffless monitoring, Multi-parameter monitoring devices, AI/ML methods, Remote monitoring
INTRODUCTION
Driven by the rise of mobile medicine, advancements in smart sensing technologies, and the growing interest in personalized health, the field of smart wearable devices has seen rapid advancements in recent years.1These wearable devices monitor and track various health parameters or deliver medical interventions. By collecting real–time health data, they offer valuable insights to both users and healthcare providers. Common types of wearable medical devices include –fitness trackers: monitor physical activity, heart rate, sleep patterns, and other fitness–related metrics; and smartwatches: equipped withhealth monitoring features such as heart rate monitoring, electrocardiography (ECG)recording, and activity tracking.
The potential to improve healthcare outcomes, enhance patient engagement, and enable more proactive management of chronic conditions is held by these devices. However, the accuracy, reliability, and security of the data collected by these devices must be ensured