Estimation and Validation of Arterial Blood Pressure Using Photoplethysmogram Morphology Features in Conjunction With Pulse Arrival Time in Large Open Databases
نویسندگان
چکیده
Although various predictors and methods for BP estimation have been proposed, differences in study designs led to difficulties determining the optimal method. This presents analyses of using 2.4 million cardiac cycles two commonly used non-invasive biosignals, electrocardiogram (ECG) photoplethysmogram (PPG), from 1376 surgical patients. Feature selection were determine best subset a total 42 including PAT, heart rate (HR), PPG morphology features, models constructed linear regression (LR), random forest (RF), artificial neural network (ANN), recurrent (RNN) evaluated. 28 features out determined as suitable estimation, particular outperformed which has conventionally seen indicator BP. By modelling low frequency component ANN high RNN with selected predictors, mean errors 0.05 ± 6.92 mmHg systolic BP, -0.05 3.99 diastolic achieved. External validation model another biosignal database consisting 334 intensive care unit patients similar results, satisfying three standards accuracy monitors. The results indicate that proposed method can contribute realization ubiquitous continuous monitoring.
منابع مشابه
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ژورنال
عنوان ژورنال: IEEE Journal of Biomedical and Health Informatics
سال: 2021
ISSN: ['2168-2208', '2168-2194']
DOI: https://doi.org/10.1109/jbhi.2020.3009658