Key result
PPG-based neural network estimates systolic blood pressure with ~5 mmHg mean absolute error.
Why the study?
Continuous blood pressure monitoring is important for managing cardiovascular diseases, and PPG offers non-invasive monitoring based on blood volume changes.
Can a PPG-based electronic sensing system using a Feed-Forward Artificial Neural Network accurately estimate systolic and diastolic blood pressure without a cuff?
Can a PPG-based electronic sensing system using a Feed-Forward Artificial Neural Network accurately estimate systolic and diastolic blood pressure without a cuff?
Answers are grounded in this paper and cite their sources.
A novel PPG-based machine learning algorithm demonstrates promising accuracy for cuff-less, calibration-free continuous blood pressure monitoring.
Continuous blood pressure monitoring is an important aid for preventing, diagnosing, and timely treating different pathologies, especially in the field of cardiovascular diseases. Photoplethysmography (PPG) is an advanced and non-invasive technology that is increasingly used in vital signs monitoring. One of the main advantages of photoplethysmography is its ability to continuously and accurately measure a biological signal, relying on the sensitivity to blood volume changes in blood vessels, which allows the real-time recording of each heartbeat and the assessment of the presence of any abnormalities. The aim of this paper is to present an innovative electronic system for cuff-less blood pressure prediction, relying on the only photoplethysmo-graphic signal. After accurate signal pre-processing which aims to remove motion artifacts and slow fluctuations, this system uses an algorithm based on Feed-Forward Artificial Neural Network (ANN) to estimate both systolic and diastolic blood pressure, using temporal and spectral features. This algorithm has been tested using a brand-new dataset, which includes multi-wavelength photopletysmographic (PPG) signals and blood pressure values derived from a reference device. The results are promising, with a Mean Absolute Error on the Test Set of 5.08 ± 8.83 mmHg and 4.37 ± 7.08 mmHg for systolic and diastolic blood pressure respectively.
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Botrugno et al. (2024) studied Blood pressure estimation. PPG-based electronic sensing system with Artificial Neural Network vs. Reference device was evaluated on Mean Absolute Error for systolic and diastolic blood pressure (MAE 5.08 ± 8.83 mmHg (systolic), 4.37 ± 7.08 mmHg (diastolic)). A PPG-based electronic sensing system using an artificial neural network estimated blood pressure with a mean absolute error of 5.08 ± 8.83 mmHg (systolic) and 4.37 ± 7.08 mmHg (diastolic).
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