Clinical trial NCT06320847: Predictive Remote Photoplethysmography Algorithm for Blood Pressure Pressure Assessment and Monitoring
Official title: Development of Predictive Remote Photoplethysmography Algorithm for Blood Pressure Assessment and Monitoring
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Remote Photoplethysmography , Blood Pressure
Lead sponsor: Singapore General Hospital
Sponsor class: OTHER
Collaborators: name: Nervotec Pte. Ltd.
First posted: 2024-03-20
Enrollment: 300
Sex: ALL
Minimum age (years): 21
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief summary: 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: To determine the correlation between remote photoplethysmography (rPPG) and blood pressure variations.
timeFrame: 1 year
measure: To develop a predictive model in using rPPG for determination of blood pressure.
timeFrame: 1 year
measure: To validate this predictive model by comparing blood pressure readings obtained using rPPG with standard procedures such as contact sensors and automated oscillometry.
timeFrame: 1 year
Secondary outcomes:
Other outcomes:
Locations:
city: Singapore
country: Singapore
facility: Singapore General Hospital
Study officials:
References: