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Detection of hypertension using a target spectral camera: a prospective clinical study
Ryoko Uchida1, Eriko Hasumi2,3, Ying Chen1
1Department of Advanced Cardiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Insights
Accurate, cuffless blood pressure (BP) monitoring is now possible using spectral camera recordings. This technology can detect hypertension with high accuracy in seconds, paving the way for simplified BP assessment.
Area of Science:
- Biomedical Engineering
- Cardiovascular Research
- Medical Imaging
Background:
- Hypertension is a major cause of premature death, necessitating early detection through regular blood pressure (BP) monitoring.
- Current BP monitoring methods often require cuffs, highlighting the need for advanced, accurate cuffless BP devices.
- Pulse transit time (PTT) is a key parameter for estimating BP, but its extraction typically requires specialized equipment.
Purpose of the Study:
- To evaluate the accuracy of blood pressure (BP) measurements derived from a target spectral camera.
- To assess the potential of using spectral imaging and machine learning for non-invasive hypertension detection.
- To determine the feasibility of rapid BP assessment using cuffless technology.
Main Methods:
- 215 adults had their palms and faces imaged using a spectral camera (640x480 pixels, 150 fps) capturing RGB wavelength data.
- Pulse transit time (PTT) was extracted from video recordings, correlating facial and palmar signals.
- A machine learning model analyzed spectral data to classify hypertension (systolic BP ≥ 130 mmHg or diastolic BP ≥ 80 mmHg), validated against continuous CNAPmonitor500 BP measurements.
Main Results:
- The machine learning model achieved 95.0% accuracy in discriminating between hypertension and normal BP within 30 seconds.
- High accuracy (90.3%) was maintained even with analysis windows as short as 5 seconds.
- Heartbeat-by-heartbeat analysis allowed for hypertension determination from just one second of camera footage or a single heartbeat.
Conclusions:
- Spectral camera imaging combined with machine learning enables accurate and rapid hypertension detection.
- The developed method offers a promising approach for simplified, cuffless blood pressure monitoring.
- This technology has the potential to significantly improve early detection of cardiovascular disease risk.
Abstract:
Hypertension is a significant contributor to premature mortality, and the regular monitoring of blood pressure (BP) enables the early detection of hypertension and cardiovascular disease. There is an urgent need for the development of highly accurate cuffless BP devices. We examined BP measurements based on a target spectral camera's recordings and evaluated their accuracy. Images of 215 adults' palms and faces were recorded, and BP was measured. The camera captured RGB wavelength data at 640 × 480 pixels and 150 frames per second (fps). These recordings were analyzed to extract pulse transit time (PTT) values between the face and palm, a key parameter for estimating BP. Continuous BP measurements were taken using a CNAPmonitor500 for validation. Three frequency wavelengths were measured from video images. A machine learning model was constructed to determine hypertension, defined as a systolic BP of 130 mmHg or higher or a diastolic BP of 80 mmHg or higher, using the visualized data. The discrimination between hypertension and normal BP was 95.0% accurate within 30 s and 90.3% within 5 s, based on the captured images. The results of heartbeat-by-heartbeat analyses can be used to determine hypertension based on only one second of camera footage or one heartbeat. The data extracted from a video recorded by a target spectral camera enabled accurate hypertension diagnoses, suggesting the potential for simplified BP monitoring.
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