Journal
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 25, Issue 4, Pages 1018-1030Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2020.3009658
Keywords
Estimation; Morphology; Databases; Feature extraction; Electrocardiography; Biological system modeling; Monitoring; Continuous blood pressure monitoring; biosignal database; photoplethysmogram morphology; feature selection; pulse arrival time
Categories
Funding
- Bio and Medical Technology Development Program of the National Research Foundation (NRF) - Korean Government (MSIT) [2016M3A9F1939646]
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This study presents and validates a blood pressure estimation method through analysis of a large amount of bio-signal data from surgical patients, indicating that this method can achieve ubiquitous non-invasive continuous blood pressure monitoring.
Although various predictors and methods for BP estimation have been proposed, differences in study designs have led to difficulties in determining the optimal method. This study presents analyses of BP estimation methods using 2.4 million cardiac cycles of two commonly used non-invasive biosignals, electrocardiogram (ECG) and photoplethysmogram (PPG), from 1376 surgical patients. Feature selection methods were used to determine the best subset of predictors from a total of 42 including PAT, heart rate (HR), and various PPG morphology features, and BP estimation models constructed using linear regression (LR), random forest (RF), artificial neural network (ANN), and recurrent neural network (RNN) were evaluated. 28 features out of 42 were determined as suitable for BP estimation, in particular two PPG morphology features outperformed PAT, which has been conventionally seen as the best non-invasive indicator of BP. By modelling the low frequency component of BP using ANN and the high frequency component using RNN with the selected predictors, mean errors of 0.05 +/- 6.92 mmHg for systolic BP, and -0.05 +/- 3.99 mmHg for diastolic BP were achieved. External validation of the model using another biosignal database consisting of 334 intensive care unit patients led to similar results, satisfying three standards for accuracy of BP monitors. The results indicate that the proposed method can contribute to the realization of ubiquitous non-invasive continuous BP monitoring.
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