A Neural Network-based method for continuous blood pressure estimation from a PPG signal

• Published in: 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) pp. 280 - 283
• Main Authors: Kurylyak, Yuriy, Lamonaca, Francesco, Grimaldi, Domenico
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2013
• Subjects: Artificial neural networks; Biomedical monitoring; Blood pressure; Estimation; hypertension; Linear regression; Monitoring; neural networks; Neurons; photoplethysmography
• ISBN: 9781467346214; 1467346217
• ISSN: 1091-5281
• DOI: 10.1109/I2MTC.2013.6555424

There is a relation, not always linear, between the blood pressure and the pulse duration, obtained from photoplethysmography (PPG) signal. In order to estimate the blood pressure from the PPG signal, in this paper the Artificial Neural Networks (ANNs) are used. Training data were extracted from the Multiparameter Intelligent Monitoring in Intensive Care waveform database for better representation of possible pulse and pressure variation. In total there were analyzed more than 15000 heartbeats and 21 parameters were extracted from each of them that define the input vector for the ANN. The comparison between estimated and reference values shows better accuracy than the linear regression method and satisfy the American National Standards of the Association for the Advancement of Medical Instrumentation.