Predictive Remote Photoplethysmography Algorithm for Blood Pressure Pressure Assessment and Monitoring

March 13, 2024 updated by: Singapore General Hospital

Development of Predictive Remote Photoplethysmography Algorithm for Blood Pressure Assessment and Monitoring

Significant advancements in the field of medical technologies have resulted in the rise of contact-free methods of haemodynamic monitoring. Remote photoplethysmography (rPPG) is a videobased, contactless form of monitoring that operates through a camera-enabled device. This innovation interprets minute variations in skin colour due to blood flow which, when analysed with complex signal processing algorithms, generates vital sign readings. Currently, Nervotec's rPPG technology allows for the collection of rPPG waveforms, which enables the measurement of heart rate, heart rate variability, respiration rate and blood oxygen saturation (SpO2) level through signal processing techniques. The plethysmography signals can be used to estimate blood pressure through the creation and training of a predictive model. By examining and extracting key features of a continuous PPG waveform by training an artificial neural network, correlations between these features and BP can be studied.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

This is a prospective, feasibility study.Background literature has shown that plethysmography (PPG) signals can be used to estimate blood pressure through the creation and training of a predictive model. By examining and extracting key features of a continuous PPG waveform by training an artificial neural network, correlations between these features and blood pressure can be studied.

Nervotec, a digital health and AI company , has successfully used rPPG technology to allow for collection of rPPG waveforms, which enables the measurement of heart rate (HR), heart rate variability (HRV), respiration rate (RR) and blood oxygen saturation (SpO2) level through signal processing techniques. They have incorporated rPPG technology into a mobile application using smartphone cameras for scanning an individual's face. However, this is yet to be established for blood pressure. We aim to perform a prospective study to evaluate the feasibility and accuracy of obtaining blood pressure measurements through rPPG.

Hypothesis: Remote photoplethysmography can be used to determine blood pressure and this algorithm can develop into a customised smartphone based application.

Study Type

Interventional

Enrollment (Estimated)

300

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Singapore
      • Singapore, Singapore, 169608
        • Recruiting
        • Singapore General Hospital
        • Contact:
        • Principal Investigator:
          • Hairil Rizal Abdullah
        • Sub-Investigator:
          • Jia Xin Chai

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

Inclusion Criteria:

  • Age ≥ 21 years old
  • Ability to provide informed consent
  • Any surgery except head and neck surgeries

Exclusion Criteria:

  • Age ≤ 21 years old
  • Inability to provide informed consent
  • Patients going for head and neck surgery

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Time Frame
To determine the correlation between remote photoplethysmography (rPPG) and blood pressure variations.
Time Frame: 1 year
1 year
To develop a predictive model in using rPPG for determination of blood pressure.
Time Frame: 1 year
1 year
To validate this predictive model by comparing blood pressure readings obtained using rPPG with standard procedures such as contact sensors and automated oscillometry.
Time Frame: 1 year
1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Singapore General Hospital

Collaborators

Nervotec Pte. Ltd.

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 2, 2024

Primary Completion (Estimated)

December 1, 2024

Study Completion (Estimated)

December 1, 2024

Study Registration Dates

First Submitted

March 13, 2024

First Submitted That Met QC Criteria

March 13, 2024

First Posted (Actual)

March 20, 2024

Study Record Updates

Last Update Posted (Actual)

March 20, 2024

Last Update Submitted That Met QC Criteria

March 13, 2024

Last Verified

March 1, 2024

More Information

Terms related to this study

Other Study ID Numbers

  • Predictbloodpressure

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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