An artificial neural network using photoplethysmogram signals estimated beat-to-beat systolic and diastolic blood pressure with an error of 0.06 ± 7.08 mmHg and 0.01 ± 4.66 mmHg, respectively.
Observational (n=92)
Does optical blood pressure estimation using PPG and FFT-based neural networks accurately estimate blood pressure compared to reference standards?
A novel PPG-based blood pressure estimation method using FFT and neural networks demonstrated high accuracy and correlation with reference measurements, offering a potential continuous, cuffless BP monitoring solution.
We introduce and validate a beat-to-beat optical blood pressure (BP) estimation paradigm using only photoplethysmogram (PPG) signal from finger tips. The scheme determines subject-specific contribution to PPG signal and removes most of its influence by proper normalization. Key features such as amplitudes and phases of cardiac components were extracted by a fast Fourier transform and were used to train an artificial neural network, which was then used to estimate BP from PPG. Validation was done on 69 patients from the MIMIC II database plus 23 volunteers. All estimations showed a good correlation with the reference values. This method is fast and robust, and can potentially be used to perform pulse wave analysis in addition to BP estimation.
Xing et al. (Tue,) conducted a observational in Blood pressure monitoring (n=92). Optical blood pressure estimation using PPG and FFT-based neural networks vs. Invasive arterial blood pressure and cuff sphygmomanometer was evaluated on Beat-to-beat fitting error for systolic blood pressure (SBP). An artificial neural network using photoplethysmogram signals estimated beat-to-beat systolic and diastolic blood pressure with an error of 0.06 ± 7.08 mmHg and 0.01 ± 4.66 mmHg, respectively.
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