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?
Population
Dataset of multi-wavelength PPG signals and reference blood pressure values
Comparison
PPG-based ANN algorithm vs reference device measurements
Design
Algorithm development and validation study
Key result
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).
Authors
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May support cuffless BP monitoring development; leaves open prospective validation before clinical adoption.
Can a PPG-based electronic sensing system using a Feed-Forward Artificial Neural Network accurately estimate systolic and diastolic blood pressure without a cuff?
Effect estimate: MAE 5.08 ± 8.83 mmHg (systolic), 4.37 ± 7.08 mmHg (diastolic)
A novel PPG-based machine learning algorithm demonstrates promising accuracy for cuff-less, calibration-free continuous blood pressure monitoring.
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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