Non-invasive Calibration-Free Blood Pressure Estimation Based on Artificial Neural Network
This paper presents a non-invasive method for Blood Pressure (BP) estimation based on extracted features from photoplethysmogram (PPG) and Electrocardiogram (ECG) signals. The proposed method depends on a machine learning technique, namely Artificial Neural Networks (ANN), to estimate blood pressure. The training is conducted on a real data set (more than 2000 BP, ECG and PPG signals) recorded by patients’ monitoring at various hospitals between 2001 and 2008. In addition to the ten features that are usually used in literature, the proposed method uses the cross validation technique between features to provide more robust estimation of the blood pressure. Furthermore, the proposed method provides accurate and reliable blood pressure estimation while it is calibration-free. Compared to previous works, we used half of the data and the results clarified that we achieved more accuracy in the systolic pressure measurements. These results are expected to improve more by increasing the training samples, which is planned in future work.
KeywordsPulse wave velocity BP monitoring Machine learning
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