A Support Vector Machine method for continuous blood pressure estimation from PPG signals demonstrated better accuracy than linear regression and ANN methods for diastolic blood pressure.
Does a Support Vector Machine (SVM) method improve the accuracy of continuous blood pressure estimation from a PPG signal compared to linear regression and ANN methods?
An SVM-based method for continuous blood pressure estimation from PPG signals provides better accuracy than linear regression and ANN methods, showing promise for mobile wearable devices.
There is not always a linear relationship between the blood pressure and the pulse duration obtained from photoplethysmography (PPG) signal. In order to estimate the blood pressure from the PPG signal, A Support Vector Machine (SVM) method for continuous blood pressure estimation from a PPG Signal is applied in this paper. Training data were extracted from The University of Queensland Vital Signs Dataset for better representation of possible pulse and pressure variation. In total there were more than 7000 heartbeats and 9 parameters to be extracted from each other for analysis, then these features were defined as the input vector for training. The comparison between estimated and reference values shows better accuracy than the linear regression method and also shows better accuracy than the ANN method in diastolic blood pressure, which brings great significance in the field of mobile wearable.
Zhang et al. (Fri,) conducted a other in Blood pressure estimation. Support Vector Machine (SVM) method vs. Linear regression and ANN methods was evaluated on Accuracy of blood pressure estimation. A Support Vector Machine method for continuous blood pressure estimation from PPG signals demonstrated better accuracy than linear regression and ANN methods for diastolic blood pressure.
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